This notebook is a template with each step that you need to complete for the project.
Please fill in your code where there are explicit ? markers in the notebook. You are welcome to add more cells and code as you see fit.
Once you have completed all the code implementations, please export your notebook as a HTML file so the reviews can view your code. Make sure you have all outputs correctly outputted.
File-> Export Notebook As... -> Export Notebook as HTML
There is a writeup to complete as well after all code implememtation is done. Please answer all questions and attach the necessary tables and charts. You can complete the writeup in either markdown or PDF.
Completing the code template and writeup template will cover all of the rubric points for this project.
The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this notebook and also discuss the results in the writeup file.
Below is example of steps to get the API username and key. Each student will have their own username and key.
kaggle.json and use the username and key.
ml.t3.medium instance (2 vCPU + 4 GiB)Python 3 (MXNet 1.8 Python 3.7 CPU Optimized)# !pip install -U pip
# !pip install -U setuptools wheel
# !pip install -U "mxnet<2.0.0" bokeh==2.0.1
# !pip install autogluon --no-cache-dir
# # Without --no-cache-dir, smaller aws instances may have trouble installing
# # create the .kaggle directory and an empty kaggle.json file
# !mkdir -p /root/.kaggle
# !touch /root/.kaggle/kaggle.json
# !chmod 600 /root/.kaggle/kaggle.json
# # Fill in your user name and key from creating the kaggle account and API token file
# import json
# kaggle_username = "FILL_IN_USERNAME"
# kaggle_key = "FILL_IN_KEY"
# # Save API token the kaggle.json file
# with open("/root/.kaggle/kaggle.json", "w") as f:
# f.write(json.dumps({"username": kaggle_username, "key": kaggle_key}))
# # Download the dataset, it will be in a .zip file so you'll need to unzip it as well.
# !kaggle competitions download -c bike-sharing-demand
# # If you already downloaded it you can use the -o command to overwrite the file
# !unzip -o bike-sharing-demand.zip
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from autogluon.tabular import TabularPredictor
import autogluon.core as ag
import warnings
warnings.filterwarnings('ignore')
# Create the train dataset in pandas by reading the csv
# Set the parsing of the datetime column so you can use some of the `dt` features in pandas later
train = pd.read_csv('bike-sharing-demand/train.csv',parse_dates =['datetime'])
train.head()
| datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | casual | registered | count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0 | 3 | 13 | 16 |
| 1 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 8 | 32 | 40 |
| 2 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 5 | 27 | 32 |
| 3 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 3 | 10 | 13 |
| 4 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 0 | 1 | 1 |
# Simple output of the train dataset to view some of the min/max/varition of the dataset features.
train.describe()
| season | holiday | workingday | weather | temp | atemp | humidity | windspeed | casual | registered | count | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 10886.000000 | 10886.000000 | 10886.000000 | 10886.000000 | 10886.00000 | 10886.000000 | 10886.000000 | 10886.000000 | 10886.000000 | 10886.000000 | 10886.000000 |
| mean | 2.506614 | 0.028569 | 0.680875 | 1.418427 | 20.23086 | 23.655084 | 61.886460 | 12.799395 | 36.021955 | 155.552177 | 191.574132 |
| std | 1.116174 | 0.166599 | 0.466159 | 0.633839 | 7.79159 | 8.474601 | 19.245033 | 8.164537 | 49.960477 | 151.039033 | 181.144454 |
| min | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 0.82000 | 0.760000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| 25% | 2.000000 | 0.000000 | 0.000000 | 1.000000 | 13.94000 | 16.665000 | 47.000000 | 7.001500 | 4.000000 | 36.000000 | 42.000000 |
| 50% | 3.000000 | 0.000000 | 1.000000 | 1.000000 | 20.50000 | 24.240000 | 62.000000 | 12.998000 | 17.000000 | 118.000000 | 145.000000 |
| 75% | 4.000000 | 0.000000 | 1.000000 | 2.000000 | 26.24000 | 31.060000 | 77.000000 | 16.997900 | 49.000000 | 222.000000 | 284.000000 |
| max | 4.000000 | 1.000000 | 1.000000 | 4.000000 | 41.00000 | 45.455000 | 100.000000 | 56.996900 | 367.000000 | 886.000000 | 977.000000 |
# Create the test pandas dataframe in pandas by reading the csv, remember to parse the datetime!
test = pd.read_csv('bike-sharing-demand/test.csv',parse_dates =['datetime'])
test.head()
| datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-20 00:00:00 | 1 | 0 | 1 | 1 | 10.66 | 11.365 | 56 | 26.0027 |
| 1 | 2011-01-20 01:00:00 | 1 | 0 | 1 | 1 | 10.66 | 13.635 | 56 | 0.0000 |
| 2 | 2011-01-20 02:00:00 | 1 | 0 | 1 | 1 | 10.66 | 13.635 | 56 | 0.0000 |
| 3 | 2011-01-20 03:00:00 | 1 | 0 | 1 | 1 | 10.66 | 12.880 | 56 | 11.0014 |
| 4 | 2011-01-20 04:00:00 | 1 | 0 | 1 | 1 | 10.66 | 12.880 | 56 | 11.0014 |
# Same thing as train and test dataset
submission = pd.read_csv('bike-sharing-demand/sampleSubmission.csv',parse_dates =['datetime'])
submission.head()
| datetime | count | |
|---|---|---|
| 0 | 2011-01-20 00:00:00 | 0 |
| 1 | 2011-01-20 01:00:00 | 0 |
| 2 | 2011-01-20 02:00:00 | 0 |
| 3 | 2011-01-20 03:00:00 | 0 |
| 4 | 2011-01-20 04:00:00 | 0 |
Requirements:
count, so it is the label we are setting.casual and registered columns as they are also not present in the test dataset. root_mean_squared_error as the metric to use for evaluation.best_quality to focus on creating the best model.train.head()
| datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | casual | registered | count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0 | 3 | 13 | 16 |
| 1 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 8 | 32 | 40 |
| 2 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 5 | 27 | 32 |
| 3 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 3 | 10 | 13 |
| 4 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 0 | 1 | 1 |
predictor = TabularPredictor('count',problem_type='regression',learner_kwargs={"ignored_columns":['casual','registered']},
eval_metric='root_mean_squared_error').fit(train,time_limit=600,presets='best_quality')
No path specified. Models will be saved in: "AutogluonModels\ag-20230531_013647\"
Presets specified: ['best_quality']
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels\ag-20230531_013647\"
AutoGluon Version: 0.7.0
Python Version: 3.8.16
Operating System: Windows
Platform Machine: AMD64
Platform Version: 10.0.19045
Train Data Rows: 10886
Train Data Columns: 11
Label Column: count
Preprocessing data ...
Using Feature Generators to preprocess the data ...
Dropping user-specified ignored columns: ['casual', 'registered']
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 7862.13 MB
Train Data (Original) Memory Usage: 0.78 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 2 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting DatetimeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('datetime', []) : 1 | ['datetime']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 5 | ['season', 'holiday', 'workingday', 'weather', 'humidity']
Types of features in processed data (raw dtype, special dtypes):
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['season', 'weather', 'humidity']
('int', ['bool']) : 2 | ['holiday', 'workingday']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
0.1s = Fit runtime
9 features in original data used to generate 13 features in processed data.
Train Data (Processed) Memory Usage: 0.98 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.1s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
To change this, specify the eval_metric parameter of Predictor()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 399.83s of the 599.9s of remaining time.
-101.5882 = Validation score (-root_mean_squared_error)
0.05s = Training runtime
0.02s = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 399.71s of the 599.78s of remaining time.
-84.1464 = Validation score (-root_mean_squared_error)
0.05s = Training runtime
0.03s = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 399.6s of the 599.67s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-131.4609 = Validation score (-root_mean_squared_error)
16.12s = Training runtime
6.0s = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 372.34s of the 572.41s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-131.0542 = Validation score (-root_mean_squared_error)
5.04s = Training runtime
0.9s = Validation runtime
Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 357.84s of the 557.91s of remaining time.
-116.5521 = Validation score (-root_mean_squared_error)
3.51s = Training runtime
0.4s = Validation runtime
Fitting model: CatBoost_BAG_L1 ... Training model for up to 353.64s of the 553.71s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-130.4612 = Validation score (-root_mean_squared_error)
134.11s = Training runtime
0.06s = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 210.48s of the 410.55s of remaining time.
-124.6023 = Validation score (-root_mean_squared_error)
1.31s = Training runtime
0.38s = Validation runtime
Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 208.49s of the 408.56s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-135.8377 = Validation score (-root_mean_squared_error)
20.38s = Training runtime
0.21s = Validation runtime
Fitting model: XGBoost_BAG_L1 ... Training model for up to 179.61s of the 379.67s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-131.6247 = Validation score (-root_mean_squared_error)
4.23s = Training runtime
0.32s = Validation runtime
Fitting model: NeuralNetTorch_BAG_L1 ... Training model for up to 165.98s of the 366.05s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-137.6134 = Validation score (-root_mean_squared_error)
99.5s = Training runtime
0.11s = Validation runtime
Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 57.96s of the 258.03s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-130.1323 = Validation score (-root_mean_squared_error)
4.88s = Training runtime
0.53s = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 359.98s of the 244.09s of remaining time.
-84.1464 = Validation score (-root_mean_squared_error)
0.61s = Training runtime
0.02s = Validation runtime
Fitting 9 L2 models ...
Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 243.44s of the 243.42s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-60.3213 = Validation score (-root_mean_squared_error)
11.38s = Training runtime
2.87s = Validation runtime
Fitting model: LightGBM_BAG_L2 ... Training model for up to 222.42s of the 222.39s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-55.077 = Validation score (-root_mean_squared_error)
3.59s = Training runtime
0.23s = Validation runtime
Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 209.32s of the 209.3s of remaining time.
-53.3799 = Validation score (-root_mean_squared_error)
11.79s = Training runtime
0.48s = Validation runtime
Fitting model: CatBoost_BAG_L2 ... Training model for up to 196.73s of the 196.72s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-55.5555 = Validation score (-root_mean_squared_error)
28.35s = Training runtime
0.05s = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L2 ... Training model for up to 159.62s of the 159.61s of remaining time.
-54.3037 = Validation score (-root_mean_squared_error)
2.7s = Training runtime
0.42s = Validation runtime
Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 156.22s of the 156.19s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-51.7163 = Validation score (-root_mean_squared_error)
21.07s = Training runtime
0.24s = Validation runtime
Fitting model: XGBoost_BAG_L2 ... Training model for up to 126.3s of the 126.29s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-54.9604 = Validation score (-root_mean_squared_error)
4.01s = Training runtime
0.11s = Validation runtime
Fitting model: NeuralNetTorch_BAG_L2 ... Training model for up to 112.81s of the 112.8s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-55.1251 = Validation score (-root_mean_squared_error)
77.48s = Training runtime
0.19s = Validation runtime
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 26.22s of the 26.2s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-55.0695 = Validation score (-root_mean_squared_error)
6.94s = Training runtime
0.23s = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 9.98s of remaining time.
-50.5274 = Validation score (-root_mean_squared_error)
0.48s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 590.54s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels\ag-20230531_013647\")
predictor.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L3 -50.545607 11.096767 409.280485 0.000000 0.361998 3 True 22
1 NeuralNetFastAI_BAG_L2 -51.716262 10.008647 322.826800 0.270120 22.167076 2 True 18
2 RandomForestMSE_BAG_L2 -53.379886 10.171751 314.397344 0.433223 13.737620 2 True 15
3 ExtraTreesMSE_BAG_L2 -54.303663 10.179574 303.527697 0.441046 2.867972 2 True 17
4 XGBoost_BAG_L2 -54.960386 9.838179 304.831909 0.099651 4.172185 2 True 19
5 LightGBMLarge_BAG_L2 -55.069468 10.038877 305.921805 0.300349 5.262080 2 True 21
6 LightGBM_BAG_L2 -55.077004 9.937626 304.271100 0.199099 3.611376 2 True 14
7 NeuralNetTorch_BAG_L2 -55.314134 9.952379 370.145819 0.213851 69.486095 2 True 20
8 CatBoost_BAG_L2 -55.555501 9.775106 330.275181 0.036578 29.615457 2 True 16
9 LightGBMXT_BAG_L2 -60.321306 13.522628 313.045133 3.784101 12.385408 2 True 13
10 WeightedEnsemble_L2 -84.146423 0.021363 0.562025 0.000000 0.503019 2 True 12
11 KNeighborsDist_BAG_L1 -84.146423 0.021363 0.059006 0.021363 0.059006 1 True 2
12 KNeighborsUnif_BAG_L1 -101.588176 0.023003 0.055419 0.023003 0.055419 1 True 1
13 RandomForestMSE_BAG_L1 -116.552091 0.380218 3.619674 0.380218 3.619674 1 True 5
14 ExtraTreesMSE_BAG_L1 -124.602302 0.371037 1.416224 0.371037 1.416224 1 True 7
15 LightGBMLarge_BAG_L1 -130.132290 0.633200 5.427717 0.633200 5.427717 1 True 11
16 CatBoost_BAG_L1 -130.461205 0.052495 139.030226 0.052495 139.030226 1 True 6
17 LightGBM_BAG_L1 -131.054162 0.934174 5.051711 0.934174 5.051711 1 True 4
18 LightGBMXT_BAG_L1 -131.460909 6.613437 16.065701 6.613437 16.065701 1 True 3
19 XGBoost_BAG_L1 -131.624665 0.349599 4.290946 0.349599 4.290946 1 True 9
20 NeuralNetFastAI_BAG_L1 -135.837680 0.237846 21.203035 0.237846 21.203035 1 True 8
21 NeuralNetTorch_BAG_L1 -137.613441 0.122154 104.440064 0.122154 104.440064 1 True 10
Number of models trained: 22
Types of models trained:
{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_RF', 'WeightedEnsembleModel', 'StackerEnsembleModel_TabularNeuralNetTorch', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_XGBoost'}
Bagging used: True (with 8 folds)
Multi-layer stack-ensembling used: True (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['season', 'weather', 'humidity']
('int', ['bool']) : 2 | ['holiday', 'workingday']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
*** End of fit() summary ***
C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\plots.py:138: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"
warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"')
{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',
'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',
'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',
'NeuralNetFastAI_BAG_L1': 'StackerEnsembleModel_NNFastAiTabular',
'XGBoost_BAG_L1': 'StackerEnsembleModel_XGBoost',
'NeuralNetTorch_BAG_L1': 'StackerEnsembleModel_TabularNeuralNetTorch',
'LightGBMLarge_BAG_L1': 'StackerEnsembleModel_LGB',
'WeightedEnsemble_L2': 'WeightedEnsembleModel',
'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',
'ExtraTreesMSE_BAG_L2': 'StackerEnsembleModel_XT',
'NeuralNetFastAI_BAG_L2': 'StackerEnsembleModel_NNFastAiTabular',
'XGBoost_BAG_L2': 'StackerEnsembleModel_XGBoost',
'NeuralNetTorch_BAG_L2': 'StackerEnsembleModel_TabularNeuralNetTorch',
'LightGBMLarge_BAG_L2': 'StackerEnsembleModel_LGB',
'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
'model_performance': {'KNeighborsUnif_BAG_L1': -101.58817625927213,
'KNeighborsDist_BAG_L1': -84.14642264302962,
'LightGBMXT_BAG_L1': -131.46090891834504,
'LightGBM_BAG_L1': -131.054161598899,
'RandomForestMSE_BAG_L1': -116.55209143577434,
'CatBoost_BAG_L1': -130.46120460893414,
'ExtraTreesMSE_BAG_L1': -124.60230209225125,
'NeuralNetFastAI_BAG_L1': -135.83768030668705,
'XGBoost_BAG_L1': -131.62466543942023,
'NeuralNetTorch_BAG_L1': -137.61344101796504,
'LightGBMLarge_BAG_L1': -130.13228993716103,
'WeightedEnsemble_L2': -84.14642264302962,
'LightGBMXT_BAG_L2': -60.32130645032012,
'LightGBM_BAG_L2': -55.07700383080456,
'RandomForestMSE_BAG_L2': -53.37988550271052,
'CatBoost_BAG_L2': -55.55550138438511,
'ExtraTreesMSE_BAG_L2': -54.30366322835222,
'NeuralNetFastAI_BAG_L2': -51.71626228633236,
'XGBoost_BAG_L2': -54.96038583691514,
'NeuralNetTorch_BAG_L2': -55.31413429144492,
'LightGBMLarge_BAG_L2': -55.06946824246353,
'WeightedEnsemble_L3': -50.54560736437441},
'model_best': 'WeightedEnsemble_L3',
'model_paths': {'KNeighborsUnif_BAG_L1': 'AutogluonModels\\ag-20230530_052724\\models\\KNeighborsUnif_BAG_L1\\',
'KNeighborsDist_BAG_L1': 'AutogluonModels\\ag-20230530_052724\\models\\KNeighborsDist_BAG_L1\\',
'LightGBMXT_BAG_L1': 'AutogluonModels\\ag-20230530_052724\\models\\LightGBMXT_BAG_L1\\',
'LightGBM_BAG_L1': 'AutogluonModels\\ag-20230530_052724\\models\\LightGBM_BAG_L1\\',
'RandomForestMSE_BAG_L1': 'AutogluonModels\\ag-20230530_052724\\models\\RandomForestMSE_BAG_L1\\',
'CatBoost_BAG_L1': 'AutogluonModels\\ag-20230530_052724\\models\\CatBoost_BAG_L1\\',
'ExtraTreesMSE_BAG_L1': 'AutogluonModels\\ag-20230530_052724\\models\\ExtraTreesMSE_BAG_L1\\',
'NeuralNetFastAI_BAG_L1': 'AutogluonModels\\ag-20230530_052724\\models\\NeuralNetFastAI_BAG_L1\\',
'XGBoost_BAG_L1': 'AutogluonModels\\ag-20230530_052724\\models\\XGBoost_BAG_L1\\',
'NeuralNetTorch_BAG_L1': 'AutogluonModels\\ag-20230530_052724\\models\\NeuralNetTorch_BAG_L1\\',
'LightGBMLarge_BAG_L1': 'AutogluonModels\\ag-20230530_052724\\models\\LightGBMLarge_BAG_L1\\',
'WeightedEnsemble_L2': 'AutogluonModels\\ag-20230530_052724\\models\\WeightedEnsemble_L2\\',
'LightGBMXT_BAG_L2': 'AutogluonModels\\ag-20230530_052724\\models\\LightGBMXT_BAG_L2\\',
'LightGBM_BAG_L2': 'AutogluonModels\\ag-20230530_052724\\models\\LightGBM_BAG_L2\\',
'RandomForestMSE_BAG_L2': 'AutogluonModels\\ag-20230530_052724\\models\\RandomForestMSE_BAG_L2\\',
'CatBoost_BAG_L2': 'AutogluonModels\\ag-20230530_052724\\models\\CatBoost_BAG_L2\\',
'ExtraTreesMSE_BAG_L2': 'AutogluonModels\\ag-20230530_052724\\models\\ExtraTreesMSE_BAG_L2\\',
'NeuralNetFastAI_BAG_L2': 'AutogluonModels\\ag-20230530_052724\\models\\NeuralNetFastAI_BAG_L2\\',
'XGBoost_BAG_L2': 'AutogluonModels\\ag-20230530_052724\\models\\XGBoost_BAG_L2\\',
'NeuralNetTorch_BAG_L2': 'AutogluonModels\\ag-20230530_052724\\models\\NeuralNetTorch_BAG_L2\\',
'LightGBMLarge_BAG_L2': 'AutogluonModels\\ag-20230530_052724\\models\\LightGBMLarge_BAG_L2\\',
'WeightedEnsemble_L3': 'AutogluonModels\\ag-20230530_052724\\models\\WeightedEnsemble_L3\\'},
'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.0554194450378418,
'KNeighborsDist_BAG_L1': 0.0590062141418457,
'LightGBMXT_BAG_L1': 16.065701246261597,
'LightGBM_BAG_L1': 5.051711320877075,
'RandomForestMSE_BAG_L1': 3.619673728942871,
'CatBoost_BAG_L1': 139.03022646903992,
'ExtraTreesMSE_BAG_L1': 1.4162240028381348,
'NeuralNetFastAI_BAG_L1': 21.2030348777771,
'XGBoost_BAG_L1': 4.290945768356323,
'NeuralNetTorch_BAG_L1': 104.44006395339966,
'LightGBMLarge_BAG_L1': 5.427717447280884,
'WeightedEnsemble_L2': 0.5030190944671631,
'LightGBMXT_BAG_L2': 12.385408163070679,
'LightGBM_BAG_L2': 3.6113758087158203,
'RandomForestMSE_BAG_L2': 13.737619876861572,
'CatBoost_BAG_L2': 29.6154568195343,
'ExtraTreesMSE_BAG_L2': 2.8679721355438232,
'NeuralNetFastAI_BAG_L2': 22.167075872421265,
'XGBoost_BAG_L2': 4.172184705734253,
'NeuralNetTorch_BAG_L2': 69.48609471321106,
'LightGBMLarge_BAG_L2': 5.262080192565918,
'WeightedEnsemble_L3': 0.3619976043701172},
'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.023003101348876953,
'KNeighborsDist_BAG_L1': 0.021363496780395508,
'LightGBMXT_BAG_L1': 6.613437175750732,
'LightGBM_BAG_L1': 0.9341740608215332,
'RandomForestMSE_BAG_L1': 0.3802177906036377,
'CatBoost_BAG_L1': 0.05249500274658203,
'ExtraTreesMSE_BAG_L1': 0.37103724479675293,
'NeuralNetFastAI_BAG_L1': 0.23784637451171875,
'XGBoost_BAG_L1': 0.34959936141967773,
'NeuralNetTorch_BAG_L1': 0.12215352058410645,
'LightGBMLarge_BAG_L1': 0.6332004070281982,
'WeightedEnsemble_L2': 0.0,
'LightGBMXT_BAG_L2': 3.7841007709503174,
'LightGBM_BAG_L2': 0.19909882545471191,
'RandomForestMSE_BAG_L2': 0.43322300910949707,
'CatBoost_BAG_L2': 0.03657817840576172,
'ExtraTreesMSE_BAG_L2': 0.44104623794555664,
'NeuralNetFastAI_BAG_L2': 0.27011966705322266,
'XGBoost_BAG_L2': 0.09965109825134277,
'NeuralNetTorch_BAG_L2': 0.2138509750366211,
'LightGBMLarge_BAG_L2': 0.30034899711608887,
'WeightedEnsemble_L3': 0.0},
'num_bag_folds': 8,
'max_stack_level': 3,
'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'KNeighborsDist_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'LightGBMXT_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'RandomForestMSE_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'CatBoost_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'NeuralNetFastAI_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'XGBoost_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'NeuralNetTorch_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBMLarge_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L2': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBMXT_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'RandomForestMSE_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'CatBoost_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'ExtraTreesMSE_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'NeuralNetFastAI_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'XGBoost_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'NeuralNetTorch_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBMLarge_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L3': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True}},
'leaderboard': model score_val pred_time_val fit_time \
0 WeightedEnsemble_L3 -50.545607 11.096767 409.280485
1 NeuralNetFastAI_BAG_L2 -51.716262 10.008647 322.826800
2 RandomForestMSE_BAG_L2 -53.379886 10.171751 314.397344
3 ExtraTreesMSE_BAG_L2 -54.303663 10.179574 303.527697
4 XGBoost_BAG_L2 -54.960386 9.838179 304.831909
5 LightGBMLarge_BAG_L2 -55.069468 10.038877 305.921805
6 LightGBM_BAG_L2 -55.077004 9.937626 304.271100
7 NeuralNetTorch_BAG_L2 -55.314134 9.952379 370.145819
8 CatBoost_BAG_L2 -55.555501 9.775106 330.275181
9 LightGBMXT_BAG_L2 -60.321306 13.522628 313.045133
10 WeightedEnsemble_L2 -84.146423 0.021363 0.562025
11 KNeighborsDist_BAG_L1 -84.146423 0.021363 0.059006
12 KNeighborsUnif_BAG_L1 -101.588176 0.023003 0.055419
13 RandomForestMSE_BAG_L1 -116.552091 0.380218 3.619674
14 ExtraTreesMSE_BAG_L1 -124.602302 0.371037 1.416224
15 LightGBMLarge_BAG_L1 -130.132290 0.633200 5.427717
16 CatBoost_BAG_L1 -130.461205 0.052495 139.030226
17 LightGBM_BAG_L1 -131.054162 0.934174 5.051711
18 LightGBMXT_BAG_L1 -131.460909 6.613437 16.065701
19 XGBoost_BAG_L1 -131.624665 0.349599 4.290946
20 NeuralNetFastAI_BAG_L1 -135.837680 0.237846 21.203035
21 NeuralNetTorch_BAG_L1 -137.613441 0.122154 104.440064
pred_time_val_marginal fit_time_marginal stack_level can_infer \
0 0.000000 0.361998 3 True
1 0.270120 22.167076 2 True
2 0.433223 13.737620 2 True
3 0.441046 2.867972 2 True
4 0.099651 4.172185 2 True
5 0.300349 5.262080 2 True
6 0.199099 3.611376 2 True
7 0.213851 69.486095 2 True
8 0.036578 29.615457 2 True
9 3.784101 12.385408 2 True
10 0.000000 0.503019 2 True
11 0.021363 0.059006 1 True
12 0.023003 0.055419 1 True
13 0.380218 3.619674 1 True
14 0.371037 1.416224 1 True
15 0.633200 5.427717 1 True
16 0.052495 139.030226 1 True
17 0.934174 5.051711 1 True
18 6.613437 16.065701 1 True
19 0.349599 4.290946 1 True
20 0.237846 21.203035 1 True
21 0.122154 104.440064 1 True
fit_order
0 22
1 18
2 15
3 17
4 19
5 21
6 14
7 20
8 16
9 13
10 12
11 2
12 1
13 5
14 7
15 11
16 6
17 4
18 3
19 9
20 8
21 10 }
best_model_name = predictor.get_model_best()
predictions = predictor.predict(test,model=best_model_name)
predictions.head()
0 24.909117 1 39.908840 2 44.751022 3 48.349266 4 50.776348 Name: count, dtype: float32
# Describe the `predictions` series to see if there are any negative values
predictions.describe()
count 6493.000000 mean 99.896164 std 88.322205 min 0.362242 25% 21.798826 50% 67.599335 75% 168.456116 max 351.272461 Name: count, dtype: float64
# How many negative values do we have?
predictions[predictions<0].count()
0
# Set them to zero
predictions[predictions<0]=0
submission["count"] = predictions
submission.to_csv("submission.csv", index=False)
!kaggle competitions submit -c bike-sharing-demand -f submission.csv -m "first raw submission"
Successfully submitted to Bike Sharing Demand
0%| | 0.00/195k [00:00<?, ?B/s] 4%|4 | 8.00k/195k [01:24<32:49, 97.1B/s] 94%|#########4| 184k/195k [01:24<00:03, 3.14kB/s] 100%|##########| 195k/195k [01:28<00:00, 2.25kB/s]
My Submissions¶!kaggle competitions submissions -c bike-sharing-demand
#| tail -n +1 | head -n 6
fileName date description status publicScore privateScore -------------- ------------------- -------------------- -------- ----------- ------------ submission.csv 2023-05-30 05:49:46 first raw submission complete 1.80425 1.80425
corr=train.corr()
sns.heatmap(corr, cmap="Blues", annot=False,
xticklabels=corr.columns,
yticklabels=corr.columns);
# Create a histogram of all features to show the distribution of each one relative to the data. This is part of the exploritory data analysis
fig,ax=plt.subplots(figsize=[20,15]);
train.hist(edgecolor='black', grid=False,ax=ax);
train.datetime.dt.day_name()
0 Saturday
1 Saturday
2 Saturday
3 Saturday
4 Saturday
...
10881 Wednesday
10882 Wednesday
10883 Wednesday
10884 Wednesday
10885 Wednesday
Name: datetime, Length: 10886, dtype: object
# create a new feature
train['year'] = train.datetime.dt.year
train['week'] = train.datetime.dt.week
train['hour'] = train.datetime.dt.hour
train['weekday'] = train.datetime.dt.day_name()
test['year'] = test.datetime.dt.year
test['week'] = test.datetime.dt.week
test['hour'] = test.datetime.dt.hour
test['weekday'] = test.datetime.dt.day_name()
train["season"] = train["season"].astype('category')
train["weather"] = train["weather"].astype('category')
test["season"] = test["season"].astype('category')
test["weather"] = test["weather"].astype('category')
train['year'] = train["year"].astype('category')
# train['week'] = train["week"].astype('category')
# train['hour'] = train["hour"].astype('category')
train['weekday'] = train["weekday"].astype('category')
test['year'] = test["year"].astype('category')
# test['week'] = test["week"].astype('category')
# test['hour'] = test["hour"].astype('category')
test['weekday'] = test["weekday"].astype('category')
# View are new feature
train.head()
| datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | casual | registered | count | year | week | hour | weekday | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0 | 3 | 13 | 16 | 2011 | 52 | 0 | Saturday |
| 1 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 8 | 32 | 40 | 2011 | 52 | 1 | Saturday |
| 2 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 5 | 27 | 32 | 2011 | 52 | 2 | Saturday |
| 3 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 3 | 10 | 13 | 2011 | 52 | 3 | Saturday |
| 4 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 0 | 1 | 1 | 2011 | 52 | 4 | Saturday |
corr=train.corr()
sns.heatmap(corr, cmap="Blues", annot=False,
xticklabels=corr.columns,
yticklabels=corr.columns);
# View histogram of all features again now with the hour feature
fig,ax=plt.subplots(figsize=[20,15]);
train.hist(edgecolor='black', grid=False,ax=ax);
predictor_new_features = TabularPredictor('count',problem_type='regression',learner_kwargs={"ignored_columns":['casual','registered']},
eval_metric='root_mean_squared_error').fit(train,time_limit=600,presets='best_quality')
No path specified. Models will be saved in: "AutogluonModels\ag-20230530_061306\"
Presets specified: ['best_quality']
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels\ag-20230530_061306\"
AutoGluon Version: 0.7.0
Python Version: 3.8.16
Operating System: Windows
Platform Machine: AMD64
Platform Version: 10.0.19045
Train Data Rows: 10886
Train Data Columns: 15
Label Column: count
Preprocessing data ...
Using Feature Generators to preprocess the data ...
Dropping user-specified ignored columns: ['casual', 'registered']
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 6493.93 MB
Train Data (Original) Memory Usage: 1.59 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 3 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Fitting DatetimeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('category', []) : 2 | ['season', 'weather']
('datetime', []) : 1 | ['datetime']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 6 | ['holiday', 'workingday', 'humidity', 'year', 'week', ...]
('object', []) : 1 | ['weekday']
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 3 | ['season', 'weather', 'weekday']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['humidity', 'week', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
0.1s = Fit runtime
13 features in original data used to generate 17 features in processed data.
Train Data (Processed) Memory Usage: 1.02 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.13s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
To change this, specify the eval_metric parameter of Predictor()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 399.81s of the 599.86s of remaining time.
-101.5882 = Validation score (-root_mean_squared_error)
0.05s = Training runtime
0.02s = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 399.69s of the 599.74s of remaining time.
-84.1464 = Validation score (-root_mean_squared_error)
0.06s = Training runtime
0.03s = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 399.58s of the 599.63s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-34.0003 = Validation score (-root_mean_squared_error)
23.42s = Training runtime
11.91s = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 363.28s of the 563.33s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.8875 = Validation score (-root_mean_squared_error)
15.95s = Training runtime
2.69s = Validation runtime
Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 336.91s of the 536.96s of remaining time.
-38.2637 = Validation score (-root_mean_squared_error)
3.91s = Training runtime
0.39s = Validation runtime
Fitting model: CatBoost_BAG_L1 ... Training model for up to 332.28s of the 532.33s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.9105 = Validation score (-root_mean_squared_error)
266.12s = Training runtime
0.14s = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 57.83s of the 257.89s of remaining time.
-37.9605 = Validation score (-root_mean_squared_error)
2.37s = Training runtime
0.44s = Validation runtime
Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 54.68s of the 254.74s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-44.205 = Validation score (-root_mean_squared_error)
23.72s = Training runtime
0.39s = Validation runtime
Fitting model: XGBoost_BAG_L1 ... Training model for up to 22.03s of the 222.09s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-34.6233 = Validation score (-root_mean_squared_error)
12.38s = Training runtime
0.71s = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 199.53s of remaining time.
-31.9455 = Validation score (-root_mean_squared_error)
0.4s = Training runtime
0.0s = Validation runtime
Fitting 9 L2 models ...
Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 199.11s of the 199.1s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-31.1964 = Validation score (-root_mean_squared_error)
4.94s = Training runtime
0.54s = Validation runtime
Fitting model: LightGBM_BAG_L2 ... Training model for up to 185.05s of the 185.04s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-30.7771 = Validation score (-root_mean_squared_error)
4.03s = Training runtime
0.24s = Validation runtime
Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 171.53s of the 171.52s of remaining time.
-31.8463 = Validation score (-root_mean_squared_error)
12.21s = Training runtime
0.46s = Validation runtime
Fitting model: CatBoost_BAG_L2 ... Training model for up to 158.53s of the 158.52s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-30.4971 = Validation score (-root_mean_squared_error)
126.81s = Training runtime
0.05s = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L2 ... Training model for up to 22.93s of the 22.93s of remaining time.
-31.6902 = Validation score (-root_mean_squared_error)
2.98s = Training runtime
0.44s = Validation runtime
Fitting model: NeuralNetFastAI_BAG_L2 ... Training model for up to 19.22s of the 19.22s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-30.3001 = Validation score (-root_mean_squared_error)
18.73s = Training runtime
0.31s = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the -8.14s of remaining time.
-29.8901 = Validation score (-root_mean_squared_error)
0.28s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 608.44s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels\ag-20230530_061306\")
predictor_new_features.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L3 -29.890078 17.328178 497.813932 0.000000 0.277118 3 True 17
1 NeuralNetFastAI_BAG_L2 -30.300083 17.032513 366.699103 0.309780 18.725494 2 True 16
2 CatBoost_BAG_L2 -30.497057 16.777017 474.781693 0.054284 126.808084 2 True 14
3 LightGBM_BAG_L2 -30.777089 16.964114 352.003236 0.241381 4.029627 2 True 12
4 LightGBMXT_BAG_L2 -31.196428 17.262372 352.911093 0.539639 4.937484 2 True 11
5 ExtraTreesMSE_BAG_L2 -31.690163 17.162664 350.954443 0.439931 2.980835 2 True 15
6 RandomForestMSE_BAG_L2 -31.846290 17.186521 360.183255 0.463788 12.209647 2 True 13
7 WeightedEnsemble_L2 -31.945469 15.874762 322.231183 0.000000 0.399840 2 True 10
8 LightGBM_BAG_L1 -33.887545 2.687760 15.945202 2.687760 15.945202 1 True 4
9 CatBoost_BAG_L1 -33.910528 0.140069 266.118776 0.140069 266.118776 1 True 6
10 LightGBMXT_BAG_L1 -34.000297 11.907729 23.421796 11.907729 23.421796 1 True 3
11 XGBoost_BAG_L1 -34.623349 0.713807 12.375826 0.713807 12.375826 1 True 9
12 ExtraTreesMSE_BAG_L1 -37.960508 0.441126 2.371667 0.441126 2.371667 1 True 7
13 RandomForestMSE_BAG_L1 -38.263675 0.394188 3.912401 0.394188 3.912401 1 True 5
14 NeuralNetFastAI_BAG_L1 -44.205036 0.391262 23.719739 0.391262 23.719739 1 True 8
15 KNeighborsDist_BAG_L1 -84.146423 0.031208 0.057342 0.031208 0.057342 1 True 2
16 KNeighborsUnif_BAG_L1 -101.588176 0.015584 0.050859 0.015584 0.050859 1 True 1
Number of models trained: 17
Types of models trained:
{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_RF', 'WeightedEnsembleModel', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_XGBoost'}
Bagging used: True (with 8 folds)
Multi-layer stack-ensembling used: True (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', []) : 3 | ['season', 'weather', 'weekday']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['humidity', 'week', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
*** End of fit() summary ***
{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',
'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',
'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',
'NeuralNetFastAI_BAG_L1': 'StackerEnsembleModel_NNFastAiTabular',
'XGBoost_BAG_L1': 'StackerEnsembleModel_XGBoost',
'WeightedEnsemble_L2': 'WeightedEnsembleModel',
'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',
'ExtraTreesMSE_BAG_L2': 'StackerEnsembleModel_XT',
'NeuralNetFastAI_BAG_L2': 'StackerEnsembleModel_NNFastAiTabular',
'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
'model_performance': {'KNeighborsUnif_BAG_L1': -101.58817625927213,
'KNeighborsDist_BAG_L1': -84.14642264302962,
'LightGBMXT_BAG_L1': -34.00029724644784,
'LightGBM_BAG_L1': -33.88754549654657,
'RandomForestMSE_BAG_L1': -38.263675022933384,
'CatBoost_BAG_L1': -33.910528160775904,
'ExtraTreesMSE_BAG_L1': -37.96050753822019,
'NeuralNetFastAI_BAG_L1': -44.205036457713064,
'XGBoost_BAG_L1': -34.623348652304394,
'WeightedEnsemble_L2': -31.945468585211955,
'LightGBMXT_BAG_L2': -31.1964283492438,
'LightGBM_BAG_L2': -30.777088546205622,
'RandomForestMSE_BAG_L2': -31.84629005491563,
'CatBoost_BAG_L2': -30.497056547941266,
'ExtraTreesMSE_BAG_L2': -31.6901633765071,
'NeuralNetFastAI_BAG_L2': -30.300082951575046,
'WeightedEnsemble_L3': -29.890077901192186},
'model_best': 'WeightedEnsemble_L3',
'model_paths': {'KNeighborsUnif_BAG_L1': 'AutogluonModels\\ag-20230530_061306\\models\\KNeighborsUnif_BAG_L1\\',
'KNeighborsDist_BAG_L1': 'AutogluonModels\\ag-20230530_061306\\models\\KNeighborsDist_BAG_L1\\',
'LightGBMXT_BAG_L1': 'AutogluonModels\\ag-20230530_061306\\models\\LightGBMXT_BAG_L1\\',
'LightGBM_BAG_L1': 'AutogluonModels\\ag-20230530_061306\\models\\LightGBM_BAG_L1\\',
'RandomForestMSE_BAG_L1': 'AutogluonModels\\ag-20230530_061306\\models\\RandomForestMSE_BAG_L1\\',
'CatBoost_BAG_L1': 'AutogluonModels\\ag-20230530_061306\\models\\CatBoost_BAG_L1\\',
'ExtraTreesMSE_BAG_L1': 'AutogluonModels\\ag-20230530_061306\\models\\ExtraTreesMSE_BAG_L1\\',
'NeuralNetFastAI_BAG_L1': 'AutogluonModels\\ag-20230530_061306\\models\\NeuralNetFastAI_BAG_L1\\',
'XGBoost_BAG_L1': 'AutogluonModels\\ag-20230530_061306\\models\\XGBoost_BAG_L1\\',
'WeightedEnsemble_L2': 'AutogluonModels\\ag-20230530_061306\\models\\WeightedEnsemble_L2\\',
'LightGBMXT_BAG_L2': 'AutogluonModels\\ag-20230530_061306\\models\\LightGBMXT_BAG_L2\\',
'LightGBM_BAG_L2': 'AutogluonModels\\ag-20230530_061306\\models\\LightGBM_BAG_L2\\',
'RandomForestMSE_BAG_L2': 'AutogluonModels\\ag-20230530_061306\\models\\RandomForestMSE_BAG_L2\\',
'CatBoost_BAG_L2': 'AutogluonModels\\ag-20230530_061306\\models\\CatBoost_BAG_L2\\',
'ExtraTreesMSE_BAG_L2': 'AutogluonModels\\ag-20230530_061306\\models\\ExtraTreesMSE_BAG_L2\\',
'NeuralNetFastAI_BAG_L2': 'AutogluonModels\\ag-20230530_061306\\models\\NeuralNetFastAI_BAG_L2\\',
'WeightedEnsemble_L3': 'AutogluonModels\\ag-20230530_061306\\models\\WeightedEnsemble_L3\\'},
'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.05085945129394531,
'KNeighborsDist_BAG_L1': 0.0573420524597168,
'LightGBMXT_BAG_L1': 23.421796083450317,
'LightGBM_BAG_L1': 15.945202350616455,
'RandomForestMSE_BAG_L1': 3.912400722503662,
'CatBoost_BAG_L1': 266.118775844574,
'ExtraTreesMSE_BAG_L1': 2.3716673851013184,
'NeuralNetFastAI_BAG_L1': 23.719738721847534,
'XGBoost_BAG_L1': 12.375825881958008,
'WeightedEnsemble_L2': 0.3998401165008545,
'LightGBMXT_BAG_L2': 4.937484264373779,
'LightGBM_BAG_L2': 4.029627323150635,
'RandomForestMSE_BAG_L2': 12.209646940231323,
'CatBoost_BAG_L2': 126.80808401107788,
'ExtraTreesMSE_BAG_L2': 2.9808349609375,
'NeuralNetFastAI_BAG_L2': 18.725494146347046,
'WeightedEnsemble_L3': 0.2771182060241699},
'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.015583992004394531,
'KNeighborsDist_BAG_L1': 0.031208038330078125,
'LightGBMXT_BAG_L1': 11.907729148864746,
'LightGBM_BAG_L1': 2.6877601146698,
'RandomForestMSE_BAG_L1': 0.39418792724609375,
'CatBoost_BAG_L1': 0.14006900787353516,
'ExtraTreesMSE_BAG_L1': 0.44112610816955566,
'NeuralNetFastAI_BAG_L1': 0.3912618160247803,
'XGBoost_BAG_L1': 0.7138073444366455,
'WeightedEnsemble_L2': 0.0,
'LightGBMXT_BAG_L2': 0.5396385192871094,
'LightGBM_BAG_L2': 0.2413806915283203,
'RandomForestMSE_BAG_L2': 0.4637877941131592,
'CatBoost_BAG_L2': 0.054283857345581055,
'ExtraTreesMSE_BAG_L2': 0.43993067741394043,
'NeuralNetFastAI_BAG_L2': 0.30977964401245117,
'WeightedEnsemble_L3': 0.0},
'num_bag_folds': 8,
'max_stack_level': 3,
'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'KNeighborsDist_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'LightGBMXT_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'RandomForestMSE_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'CatBoost_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'NeuralNetFastAI_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'XGBoost_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L2': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBMXT_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'RandomForestMSE_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'CatBoost_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'ExtraTreesMSE_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True,
'use_child_oof': True},
'NeuralNetFastAI_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L3': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True}},
'leaderboard': model score_val pred_time_val fit_time \
0 WeightedEnsemble_L3 -29.890078 17.328178 497.813932
1 NeuralNetFastAI_BAG_L2 -30.300083 17.032513 366.699103
2 CatBoost_BAG_L2 -30.497057 16.777017 474.781693
3 LightGBM_BAG_L2 -30.777089 16.964114 352.003236
4 LightGBMXT_BAG_L2 -31.196428 17.262372 352.911093
5 ExtraTreesMSE_BAG_L2 -31.690163 17.162664 350.954443
6 RandomForestMSE_BAG_L2 -31.846290 17.186521 360.183255
7 WeightedEnsemble_L2 -31.945469 15.874762 322.231183
8 LightGBM_BAG_L1 -33.887545 2.687760 15.945202
9 CatBoost_BAG_L1 -33.910528 0.140069 266.118776
10 LightGBMXT_BAG_L1 -34.000297 11.907729 23.421796
11 XGBoost_BAG_L1 -34.623349 0.713807 12.375826
12 ExtraTreesMSE_BAG_L1 -37.960508 0.441126 2.371667
13 RandomForestMSE_BAG_L1 -38.263675 0.394188 3.912401
14 NeuralNetFastAI_BAG_L1 -44.205036 0.391262 23.719739
15 KNeighborsDist_BAG_L1 -84.146423 0.031208 0.057342
16 KNeighborsUnif_BAG_L1 -101.588176 0.015584 0.050859
pred_time_val_marginal fit_time_marginal stack_level can_infer \
0 0.000000 0.277118 3 True
1 0.309780 18.725494 2 True
2 0.054284 126.808084 2 True
3 0.241381 4.029627 2 True
4 0.539639 4.937484 2 True
5 0.439931 2.980835 2 True
6 0.463788 12.209647 2 True
7 0.000000 0.399840 2 True
8 2.687760 15.945202 1 True
9 0.140069 266.118776 1 True
10 11.907729 23.421796 1 True
11 0.713807 12.375826 1 True
12 0.441126 2.371667 1 True
13 0.394188 3.912401 1 True
14 0.391262 23.719739 1 True
15 0.031208 0.057342 1 True
16 0.015584 0.050859 1 True
fit_order
0 17
1 16
2 14
3 12
4 11
5 15
6 13
7 10
8 4
9 6
10 3
11 9
12 7
13 5
14 8
15 2
16 1 }
predictions_new_features = predictor_new_features.predict(test,model=predictor_new_features.get_model_best())
predictions_new_features.head()
0 14.893423 1 7.717738 2 6.931653 3 7.115042 4 7.138554 Name: count, dtype: float32
# Remember to set all negative values to zero
predictions_new_features.describe()
count 6493.000000 mean 148.880478 std 124.469337 min -0.585657 25% 54.533398 50% 119.668686 75% 207.174530 max 785.126709 Name: count, dtype: float64
predictions_new_features[predictions_new_features<0].count()
6
predictions_new_features[predictions_new_features<0]=0
submission_new_features = submission.copy(deep=True)
# Same submitting predictions
submission_new_features["count"] = predictions_new_features
submission_new_features.to_csv("submission_new_features.csv", index=False)
!kaggle competitions submit -c bike-sharing-demand -f submission_new_features.csv -m "new features"
Successfully submitted to Bike Sharing Demand
0%| | 0.00/194k [00:00<?, ?B/s] 4%|4 | 8.00k/194k [01:24<32:47, 97.1B/s] 95%|#########4| 184k/194k [01:24<00:03, 3.14kB/s] 100%|##########| 194k/194k [01:27<00:00, 2.27kB/s]
!kaggle competitions submissions -c bike-sharing-demand
#| tail -n +1 | head -n 6
fileName date description status publicScore privateScore --------------------------- ------------------- -------------------- -------- ----------- ------------ submission_new_features.csv 2023-05-30 06:32:08 new features complete 0.70588 0.70588 submission.csv 2023-05-30 05:49:46 first raw submission complete 1.80425 1.80425
hyperparameter and hyperparameter_tune_kwargs arguments.time_limit = 10*60 # train various models for 10 mins
num_trials = 10 # try at most 10 different hyperparameter configurations for each type of model
search_strategy = 'auto' #"bayes", # random # to tune hyperparameters using Bayesian optimization routine with a local scheduler
hyperparameter_tune_kwargs = {
'num_trials': num_trials,
'scheduler' : "ASHA", #'local', #
'searcher': search_strategy,
}
predictor_new_hpo = TabularPredictor('count',problem_type='regression',learner_kwargs={"ignored_columns":['casual','registered']},
eval_metric='root_mean_squared_error').fit(train,time_limit=600,presets='best_quality',
hyperparameter_tune_kwargs=hyperparameter_tune_kwargs)
No model was trained during hyperparameter tuning NeuralNetTorch_BAG_L2... Skipping this model.
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 29.98s of the 235.49s of remaining time.
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.8223 = Validation score (-root_mean_squared_error)
8.56s = Training runtime
0.22s = Validation runtime
Repeating k-fold bagging: 2/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 222.75s of the 222.74s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.5555 = Validation score (-root_mean_squared_error)
12.48s = Training runtime
0.44s = Validation runtime
Repeating k-fold bagging: 3/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 209.52s of the 209.51s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.4301 = Validation score (-root_mean_squared_error)
16.49s = Training runtime
0.68s = Validation runtime
Repeating k-fold bagging: 4/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 196.24s of the 196.23s of remaining time.
Fitting 8 child models (S4F1 - S4F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3767 = Validation score (-root_mean_squared_error)
20.28s = Training runtime
0.94s = Validation runtime
Repeating k-fold bagging: 5/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 183.23s of the 183.22s of remaining time.
Fitting 8 child models (S5F1 - S5F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3831 = Validation score (-root_mean_squared_error)
24.0s = Training runtime
1.13s = Validation runtime
Repeating k-fold bagging: 6/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 170.37s of the 170.36s of remaining time.
Fitting 8 child models (S6F1 - S6F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3714 = Validation score (-root_mean_squared_error)
27.88s = Training runtime
1.37s = Validation runtime
Repeating k-fold bagging: 7/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 157.21s of the 157.2s of remaining time.
Fitting 8 child models (S7F1 - S7F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3516 = Validation score (-root_mean_squared_error)
31.78s = Training runtime
1.59s = Validation runtime
Repeating k-fold bagging: 8/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 144.23s of the 144.22s of remaining time.
Fitting 8 child models (S8F1 - S8F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3586 = Validation score (-root_mean_squared_error)
35.57s = Training runtime
1.79s = Validation runtime
Repeating k-fold bagging: 9/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 130.74s of the 130.73s of remaining time.
Fitting 8 child models (S9F1 - S9F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3815 = Validation score (-root_mean_squared_error)
39.77s = Training runtime
2.03s = Validation runtime
Repeating k-fold bagging: 10/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 117.37s of the 117.36s of remaining time.
Fitting 8 child models (S10F1 - S10F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3955 = Validation score (-root_mean_squared_error)
43.75s = Training runtime
2.22s = Validation runtime
Repeating k-fold bagging: 11/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 104.16s of the 104.15s of remaining time.
Fitting 8 child models (S11F1 - S11F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3799 = Validation score (-root_mean_squared_error)
47.86s = Training runtime
2.45s = Validation runtime
Repeating k-fold bagging: 12/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 90.81s of the 90.8s of remaining time.
Fitting 8 child models (S12F1 - S12F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.4001 = Validation score (-root_mean_squared_error)
51.86s = Training runtime
2.64s = Validation runtime
Repeating k-fold bagging: 13/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 77.19s of the 77.18s of remaining time.
Fitting 8 child models (S13F1 - S13F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3765 = Validation score (-root_mean_squared_error)
55.88s = Training runtime
2.83s = Validation runtime
Repeating k-fold bagging: 14/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 63.8s of the 63.79s of remaining time.
Fitting 8 child models (S14F1 - S14F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3778 = Validation score (-root_mean_squared_error)
59.89s = Training runtime
3.05s = Validation runtime
Repeating k-fold bagging: 15/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 50.53s of the 50.52s of remaining time.
Fitting 8 child models (S15F1 - S15F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3843 = Validation score (-root_mean_squared_error)
63.89s = Training runtime
3.26s = Validation runtime
Repeating k-fold bagging: 16/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 36.86s of the 36.85s of remaining time.
Fitting 8 child models (S16F1 - S16F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3761 = Validation score (-root_mean_squared_error)
67.85s = Training runtime
3.49s = Validation runtime
Repeating k-fold bagging: 17/20
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 23.46s of the 23.45s of remaining time.
Fitting 8 child models (S17F1 - S17F8) | Fitting with ParallelLocalFoldFittingStrategy
-33.3623 = Validation score (-root_mean_squared_error)
71.98s = Training runtime
3.72s = Validation runtime
Completed 17/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 9.59s of remaining time.
-33.3623 = Validation score (-root_mean_squared_error)
0.0s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 590.44s ... Best model: "WeightedEnsemble_L2"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels\ag-20230531_004701\")
predictor_new_hpo.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L2 -32.375607 3.666819 196.446225 0.000000 0.158715 2 True 4
1 LightGBMLarge_BAG_L1 -32.552075 3.557436 34.594732 3.557436 34.594732 1 True 3
2 LightGBMLarge_BAG_L2 -33.362305 7.653478 313.601118 3.721043 71.981003 2 True 5
3 WeightedEnsemble_L3 -33.362305 7.654464 313.603590 0.000986 0.002472 3 True 6
4 NeuralNetTorch_BAG_L1\6349b722 -45.199272 0.109383 161.692777 0.109383 161.692777 1 True 2
5 NeuralNetFastAI_BAG_L1\5aea9def -46.628290 0.265617 45.332606 0.265617 45.332606 1 True 1
Number of models trained: 6
Types of models trained:
{'WeightedEnsembleModel', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_TabularNeuralNetTorch', 'StackerEnsembleModel_LGB'}
Bagging used: True (with 8 folds)
Multi-layer stack-ensembling used: True (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', []) : 3 | ['season', 'weather', 'weekday']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['humidity', 'week', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
*** End of fit() summary ***
{'model_types': {'NeuralNetFastAI_BAG_L1\\5aea9def': 'StackerEnsembleModel_NNFastAiTabular',
'NeuralNetTorch_BAG_L1\\6349b722': 'StackerEnsembleModel_TabularNeuralNetTorch',
'LightGBMLarge_BAG_L1': 'StackerEnsembleModel_LGB',
'WeightedEnsemble_L2': 'WeightedEnsembleModel',
'LightGBMLarge_BAG_L2': 'StackerEnsembleModel_LGB',
'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
'model_performance': {'NeuralNetFastAI_BAG_L1\\5aea9def': -46.6282896132067,
'NeuralNetTorch_BAG_L1\\6349b722': -45.199272049199045,
'LightGBMLarge_BAG_L1': -32.552074649850724,
'WeightedEnsemble_L2': -32.37560661696187,
'LightGBMLarge_BAG_L2': -33.3623048521261,
'WeightedEnsemble_L3': -33.3623048521261},
'model_best': 'WeightedEnsemble_L2',
'model_paths': {'NeuralNetFastAI_BAG_L1\\5aea9def': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_004701\\models\\NeuralNetFastAI_BAG_L1\\5aea9def\\',
'NeuralNetTorch_BAG_L1\\6349b722': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_004701\\models\\NeuralNetTorch_BAG_L1\\6349b722\\',
'LightGBMLarge_BAG_L1': 'AutogluonModels\\ag-20230531_004701\\models\\LightGBMLarge_BAG_L1\\',
'WeightedEnsemble_L2': 'AutogluonModels\\ag-20230531_004701\\models\\WeightedEnsemble_L2\\',
'LightGBMLarge_BAG_L2': 'AutogluonModels\\ag-20230531_004701\\models\\LightGBMLarge_BAG_L2\\',
'WeightedEnsemble_L3': 'AutogluonModels\\ag-20230531_004701\\models\\WeightedEnsemble_L3\\'},
'model_fit_times': {'NeuralNetFastAI_BAG_L1\\5aea9def': 45.332605600357056,
'NeuralNetTorch_BAG_L1\\6349b722': 161.69277715682983,
'LightGBMLarge_BAG_L1': 34.5947322845459,
'WeightedEnsemble_L2': 0.15871524810791016,
'LightGBMLarge_BAG_L2': 71.98100304603577,
'WeightedEnsemble_L3': 0.0024721622467041016},
'model_pred_times': {'NeuralNetFastAI_BAG_L1\\5aea9def': 0.26561689376831055,
'NeuralNetTorch_BAG_L1\\6349b722': 0.10938262939453125,
'LightGBMLarge_BAG_L1': 3.557435989379883,
'WeightedEnsemble_L2': 0.0,
'LightGBMLarge_BAG_L2': 3.7210428714752197,
'WeightedEnsemble_L3': 0.000985860824584961},
'num_bag_folds': 8,
'max_stack_level': 3,
'model_hyperparams': {'NeuralNetFastAI_BAG_L1\\5aea9def': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'NeuralNetTorch_BAG_L1\\6349b722': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBMLarge_BAG_L1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L2': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBMLarge_BAG_L2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L3': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True}},
'leaderboard': model score_val pred_time_val fit_time \
0 WeightedEnsemble_L2 -32.375607 3.666819 196.446225
1 LightGBMLarge_BAG_L1 -32.552075 3.557436 34.594732
2 LightGBMLarge_BAG_L2 -33.362305 7.653478 313.601118
3 WeightedEnsemble_L3 -33.362305 7.654464 313.603590
4 NeuralNetTorch_BAG_L1\6349b722 -45.199272 0.109383 161.692777
5 NeuralNetFastAI_BAG_L1\5aea9def -46.628290 0.265617 45.332606
pred_time_val_marginal fit_time_marginal stack_level can_infer \
0 0.000000 0.158715 2 True
1 3.557436 34.594732 1 True
2 3.721043 71.981003 2 True
3 0.000986 0.002472 3 True
4 0.109383 161.692777 1 True
5 0.265617 45.332606 1 True
fit_order
0 4
1 3
2 5
3 6
4 2
5 1 }
predictions_new_features_hpo = predictor_new_hpo.predict(test,model=predictor_new_hpo.get_model_best())
predictions_new_features_hpo.head()
0 13.315242 1 -1.718998 2 -4.141519 3 0.576120 4 0.718507 Name: count, dtype: float32
# Remember to set all negative values to zero
predictions_new_features_hpo.describe()
count 6493.000000 mean 191.578339 std 172.726730 min -6.694787 25% 45.578114 50% 150.683228 75% 284.875244 max 876.015808 Name: count, dtype: float64
predictions_new_features_hpo[predictions_new_features_hpo<0].count()
58
predictions_new_features_hpo[predictions_new_features_hpo<0]=0
submission_new_hpo = submission.copy(deep=True)
# Same submitting predictions
submission_new_hpo["count"] = predictions_new_features_hpo
submission_new_hpo.to_csv("submission_new_hpo.csv", index=False)
!kaggle competitions submit -c bike-sharing-demand -f submission_new_hpo.csv -m "new features with hyperparameters"
Successfully submitted to Bike Sharing Demand
0%| | 0.00/194k [00:00<?, ?B/s] 4%|4 | 8.00k/194k [01:24<32:48, 96.9B/s] 95%|#########4| 184k/194k [01:24<00:03, 3.13kB/s] 100%|##########| 194k/194k [01:26<00:00, 2.29kB/s]
!kaggle competitions submissions -c bike-sharing-demand
#| tail -n +1 | head -n 6
fileName date description status publicScore privateScore --------------------------- ------------------- --------------------------------- -------- ----------- ------------ submission_new_hpo.csv 2023-05-31 00:59:18 new features with hyperparameters complete 0.49844 0.49844 submission_new_features.csv 2023-05-30 06:32:08 new features complete 0.70588 0.70588 submission.csv 2023-05-30 05:49:46 first raw submission complete 1.80425 1.80425
nn_options = { # specifies non-default hyperparameter values for neural network models
'num_epochs': 10, # number of training epochs (controls training time of NN models)
'learning_rate': ag.space.Real(1e-3, 1e-2, default=5e-3, log=True), # learning rate used in training (real-valued hyperparameter searched on log-scale)
'activation': ag.space.Categorical('relu', 'softrelu', 'tanh'), # activation function used in NN (categorical hyperparameter, default = first entry)
'layers': ag.space.Categorical([100],[200, 100]), # each choice for categorical hyperparameter 'layers' corresponds to list of sizes for each NN layer to use
'dropout_prob': ag.space.Real(0.0, 0.5, default=0.1), # dropout probability (real-valued hyperparameter)
}
gbm_options = { # specifies non-default hyperparameter values for lightGBM gradient boosted trees
'num_boost_round': 80, # number of boosting rounds (controls training time of GBM models)
'num_leaves': ag.space.Int(lower=10, upper=50, default=20), # number of leaves in trees (integer hyperparameter)
}
hyperparameters = { # hyperparameters of each model type
'GBM': gbm_options,
'NN_TORCH': nn_options,
}
time_limit = 10*60 # train various models for ~2 min
num_trials = 10 # try at most 5 different hyperparameter configurations for each type of model
search_strategy = 'auto' # to tune hyperparameters using Bayesian optimization routine with a local scheduler
hyperparameter_tune_kwargs = {
'num_trials': num_trials,
'scheduler' : 'local',
'searcher': search_strategy,
}
predictor_new_hpo1 = TabularPredictor('count',problem_type='regression',learner_kwargs={"ignored_columns":['casual','registered']},
eval_metric='root_mean_squared_error').fit(train,time_limit=time_limit,presets='best_quality',
hyperparameters=hyperparameters, hyperparameter_tune_kwargs=hyperparameter_tune_kwargs)
No path specified. Models will be saved in: "AutogluonModels\ag-20230531_025623\"
Presets specified: ['best_quality']
Warning: hyperparameter tuning is currently experimental and may cause the process to hang.
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels\ag-20230531_025623\"
AutoGluon Version: 0.7.0
Python Version: 3.8.16
Operating System: Windows
Platform Machine: AMD64
Platform Version: 10.0.19045
Train Data Rows: 10886
Train Data Columns: 15
Label Column: count
Preprocessing data ...
Using Feature Generators to preprocess the data ...
Dropping user-specified ignored columns: ['casual', 'registered']
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 8304.23 MB
Train Data (Original) Memory Usage: 0.83 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 3 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Fitting DatetimeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('category', []) : 4 | ['season', 'weather', 'year', 'weekday']
('datetime', []) : 1 | ['datetime']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 5 | ['holiday', 'workingday', 'humidity', 'week', 'hour']
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 3 | ['season', 'weather', 'weekday']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['humidity', 'week', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
0.1s = Fit runtime
13 features in original data used to generate 17 features in processed data.
Train Data (Processed) Memory Usage: 1.02 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.12s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
To change this, specify the eval_metric parameter of Predictor()
AutoGluon will fit 2 stack levels (L1 to L2) ...
WARNING: "NN" model has been deprecated in v0.4.0 and renamed to "NN_MXNET". Starting in v0.6.0, specifying "NN" or "NN_MXNET" will raise an exception. Consider instead specifying "NN_TORCH".
Fitting 2 L1 models ...
Hyperparameter tuning model: LightGBM_BAG_L1 ... Tuning model for up to 179.92s of the 599.88s of remaining time.
0%| | 0/10 [00:00<?, ?it/s]
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitted model: LightGBM_BAG_L1\T1 ... -48.1213 = Validation score (-root_mean_squared_error) 10.23s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L1\T2 ... -48.8559 = Validation score (-root_mean_squared_error) 11.31s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L1\T3 ... -41.4635 = Validation score (-root_mean_squared_error) 11.41s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L1\T4 ... -140.4194 = Validation score (-root_mean_squared_error) 11.24s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L1\T5 ... -52.1338 = Validation score (-root_mean_squared_error) 11.52s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L1\T6 ... -123.4752 = Validation score (-root_mean_squared_error) 11.35s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L1\T7 ... -48.7354 = Validation score (-root_mean_squared_error) 11.02s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L1\T8 ... -37.9529 = Validation score (-root_mean_squared_error) 11.21s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L1\T9 ... -131.3383 = Validation score (-root_mean_squared_error) 11.13s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L1\T10 ... -39.8167 = Validation score (-root_mean_squared_error) 11.27s = Training runtime 0.0s = Validation runtime Hyperparameter tuning model: NeuralNetMXNet_BAG_L1 ... Tuning model for up to 179.92s of the 487.95s of remaining time.
0%| | 0/10 [00:00<?, ?it/s]
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=31724, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=31724, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=31724, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=31724, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:58:25,251 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:25,252 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:25,252 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=35524, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=35524, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:58:25,252 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=30484, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=30484, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:58:25,252 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:25,267 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:25,273 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=11948, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=11948, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=11948, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=11948, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:58:35,031 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=21156, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=21156, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:58:35,037 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=16048, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=16048, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:58:35,044 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:35,052 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:35,057 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:35,058 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:35,065 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=25204, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=25204, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=25204, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=25204, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:58:44,077 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:44,082 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:44,087 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:44,092 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:44,096 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:44,103 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:44,106 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=34432, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=34432, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=34432, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=34432, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:58:53,474 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=33764, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=33764, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:58:53,477 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:53,481 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=18588, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=18588, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:58:53,481 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=26948, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=26948, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy 2023-05-31 07:58:53,497 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=35148, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=35148, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:58:53,500 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:58:53,502 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=18052, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=18052, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` ray::_ray_fit() (pid=34824, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=34824, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=34824, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=34824, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:59:03,123 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:03,129 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy 2023-05-31 07:59:03,146 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:03,149 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:03,155 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:03,160 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:03,165 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. ray::_ray_fit() (pid=15848, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=15848, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=15848, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=15848, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:59:11,996 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:12,005 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:12,009 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:12,014 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:12,017 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:12,021 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy 2023-05-31 07:59:12,040 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. ray::_ray_fit() (pid=35588, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=35588, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=35588, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=35588, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:59:21,215 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:21,219 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:21,225 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:21,231 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:21,239 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:21,242 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:21,272 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=11756, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=11756, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=11756, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=11756, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:59:30,477 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:30,483 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:30,488 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:30,490 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:30,499 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:30,502 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:30,507 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=21964, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=21964, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=21964, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=21964, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 07:59:39,565 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:39,583 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:39,588 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:39,594 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:39,598 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:39,602 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:39,605 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=27000, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=27000, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=27000, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=27000, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` No model was trained during hyperparameter tuning NeuralNetMXNet_BAG_L1... Skipping this model. Completed 1/20 k-fold bagging repeats ... 2023-05-31 07:59:48,948 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:48,948 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:48,948 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:48,963 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:48,963 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:48,963 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 07:59:48,963 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 394.53s of remaining time. -37.5413 = Validation score (-root_mean_squared_error) 0.33s = Training runtime 0.0s = Validation runtime WARNING: "NN" model has been deprecated in v0.4.0 and renamed to "NN_MXNET". Starting in v0.6.0, specifying "NN" or "NN_MXNET" will raise an exception. Consider instead specifying "NN_TORCH". Fitting 2 L2 models ... Hyperparameter tuning model: LightGBM_BAG_L2 ... Tuning model for up to 177.38s of the 394.17s of remaining time.
0%| | 0/10 [00:00<?, ?it/s]
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy Fitted model: LightGBM_BAG_L2\T1 ... -37.2839 = Validation score (-root_mean_squared_error) 11.08s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L2\T2 ... -36.9719 = Validation score (-root_mean_squared_error) 11.68s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L2\T3 ... -37.0125 = Validation score (-root_mean_squared_error) 11.56s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L2\T4 ... -115.2035 = Validation score (-root_mean_squared_error) 11.2s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L2\T5 ... -38.6166 = Validation score (-root_mean_squared_error) 11.37s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L2\T6 ... -111.8066 = Validation score (-root_mean_squared_error) 11.08s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L2\T7 ... -36.9637 = Validation score (-root_mean_squared_error) 11.01s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L2\T8 ... -36.5962 = Validation score (-root_mean_squared_error) 11.24s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L2\T9 ... -103.2097 = Validation score (-root_mean_squared_error) 11.07s = Training runtime 0.0s = Validation runtime Fitted model: LightGBM_BAG_L2\T10 ... -36.7172 = Validation score (-root_mean_squared_error) 11.18s = Training runtime 0.0s = Validation runtime Hyperparameter tuning model: NeuralNetMXNet_BAG_L2 ... Tuning model for up to 177.38s of the 281.45s of remaining time.
0%| | 0/10 [00:00<?, ?it/s]
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=15272, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=15272, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=15272, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=15272, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:01:52,463 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=29056, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=29056, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:01:52,468 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=24748, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=24748, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:01:52,472 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=12428, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=12428, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:01:52,475 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=35860, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=35860, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:01:52,478 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:01:52,482 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:01:52,485 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=14068, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=14068, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=14068, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=14068, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:02:03,281 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=19004, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=19004, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:02:03,312 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:03,327 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:03,333 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:03,338 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:03,343 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:03,347 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=15872, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=15872, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=15872, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=15872, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:02:12,750 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=6252, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=6252, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:02:12,754 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:12,757 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:12,761 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:12,765 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:12,770 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:12,775 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=20268, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=20268, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=20268, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=20268, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:02:21,907 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=24276, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=24276, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:02:21,918 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:21,921 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:21,921 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:21,929 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:21,934 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:21,938 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=21556, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=21556, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=21556, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=21556, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:02:31,743 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=21244, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=21244, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:02:31,754 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:31,759 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:31,764 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:31,769 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:31,773 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:31,777 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=16464, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=16464, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=16464, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=16464, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:02:40,925 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:40,929 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=15708, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=15708, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:02:40,932 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:40,935 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:40,941 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:40,945 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:40,949 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=20376, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=20376, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=20376, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=20376, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:02:49,869 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:49,869 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:49,869 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:49,869 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:49,885 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:49,885 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:49,885 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=29528, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=29528, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=29528, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=29528, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:02:58,726 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:58,733 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:58,733 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:58,740 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:58,747 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:58,747 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:02:58,754 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=16200, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=16200, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=16200, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=16200, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:03:07,850 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:03:07,853 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:03:07,857 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:03:07,858 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=10052, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=10052, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` 2023-05-31 08:03:07,866 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:03:07,871 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:03:07,875 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy ray::_ray_fit() (pid=15076, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=15076, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` Traceback (most recent call last): File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 43, in model_trial model = fit_and_save_model( File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 101, in fit_and_save_model model.fit(**fit_args, time_limit=time_left) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 154, in _fit return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 248, in _fit self._fit_folds(X=X, y=y, model_base=model_base, X_pseudo=X_pseudo, y_pseudo=y_pseudo, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 540, in _fit_folds fold_fitting_strategy.after_all_folds_scheduled() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 537, in after_all_folds_scheduled raise processed_exception File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 505, in after_all_folds_scheduled time_end_fit, predict_time, predict_1_time = self.ray.get(finished) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\client_mode_hook.py", line 105, in wrapper return func(*args, **kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\_private\worker.py", line 2309, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(ImportError): ray::_ray_fit() (pid=15076, ip=127.0.0.1) ModuleNotFoundError: No module named 'mxnet' During handling of the above exception, another exception occurred: ray::_ray_fit() (pid=15076, ip=127.0.0.1) File "python\ray\_raylet.pyx", line 823, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 875, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 830, in ray._raylet.execute_task File "python\ray\_raylet.pyx", line 834, in ray._raylet.execute_task File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 374, in _ray_fit fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 703, in fit out = self._fit(**kwargs) File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\tabular\models\tabular_nn\mxnet\tabular_nn_mxnet.py", line 135, in _fit try_import_mxnet() File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\utils\try_import.py", line 49, in try_import_mxnet raise ImportError( ImportError: Unable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade` No model was trained during hyperparameter tuning NeuralNetMXNet_BAG_L2... Skipping this model. Completed 1/20 k-fold bagging repeats ... 2023-05-31 08:03:17,113 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:03:17,117 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:03:17,121 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 186.45s of remaining time. 2023-05-31 08:03:17,128 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:03:17,132 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:03:17,137 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. 2023-05-31 08:03:17,142 ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information. -36.4283 = Validation score (-root_mean_squared_error) 0.31s = Training runtime 0.0s = Validation runtime AutoGluon training complete, total runtime = 413.92s ... Best model: "WeightedEnsemble_L3" TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels\ag-20230531_025623\")
predictor_new_hpo1.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L3 -36.428313 0.001001 146.086720 0.000000 0.305657 3 True 22
1 LightGBM_BAG_L2\T8 -36.596210 0.001001 122.927084 0.000000 11.239582 2 True 19
2 LightGBM_BAG_L2\T10 -36.717208 0.001001 122.865482 0.000000 11.177980 2 True 21
3 LightGBM_BAG_L2\T7 -36.963696 0.001001 122.702333 0.000000 11.014831 2 True 18
4 LightGBM_BAG_L2\T2 -36.971947 0.001001 123.363501 0.000000 11.675999 2 True 13
5 LightGBM_BAG_L2\T3 -37.012459 0.001001 123.246503 0.000000 11.559000 2 True 14
6 LightGBM_BAG_L2\T1 -37.283893 0.001001 122.771036 0.000000 11.083534 2 True 12
7 WeightedEnsemble_L2 -37.541253 0.000000 22.808565 0.000000 0.326270 2 True 11
8 LightGBM_BAG_L1\T8 -37.952891 0.000000 11.213756 0.000000 11.213756 1 True 8
9 LightGBM_BAG_L2\T5 -38.616601 0.001001 123.054728 0.000000 11.367225 2 True 16
10 LightGBM_BAG_L1\T10 -39.816719 0.000000 11.268540 0.000000 11.268540 1 True 10
11 LightGBM_BAG_L1\T3 -41.463544 0.000000 11.409003 0.000000 11.409003 1 True 3
12 LightGBM_BAG_L1\T1 -48.121313 0.000000 10.226465 0.000000 10.226465 1 True 1
13 LightGBM_BAG_L1\T7 -48.735404 0.000000 11.016189 0.000000 11.016189 1 True 7
14 LightGBM_BAG_L1\T2 -48.855901 0.000000 11.310180 0.000000 11.310180 1 True 2
15 LightGBM_BAG_L1\T5 -52.133844 0.000000 11.518472 0.000000 11.518472 1 True 5
16 LightGBM_BAG_L2\T9 -103.209737 0.001001 122.754920 0.000000 11.067417 2 True 20
17 LightGBM_BAG_L2\T6 -111.806629 0.001001 122.767490 0.000000 11.079988 2 True 17
18 LightGBM_BAG_L2\T4 -115.203453 0.001001 122.883715 0.000000 11.196213 2 True 15
19 LightGBM_BAG_L1\T6 -123.475210 0.000000 11.351787 0.000000 11.351787 1 True 6
20 LightGBM_BAG_L1\T9 -131.338336 0.000000 11.133766 0.000000 11.133766 1 True 9
21 LightGBM_BAG_L1\T4 -140.419440 0.001001 11.239344 0.001001 11.239344 1 True 4
Number of models trained: 22
Types of models trained:
{'StackerEnsembleModel_LGB', 'WeightedEnsembleModel'}
Bagging used: True (with 8 folds)
Multi-layer stack-ensembling used: True (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', []) : 3 | ['season', 'weather', 'weekday']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['humidity', 'week', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
*** End of fit() summary ***
{'model_types': {'LightGBM_BAG_L1\\T1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T3': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T4': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T5': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T6': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T7': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T8': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T9': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T10': 'StackerEnsembleModel_LGB',
'WeightedEnsemble_L2': 'WeightedEnsembleModel',
'LightGBM_BAG_L2\\T1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T3': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T4': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T5': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T6': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T7': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T8': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T9': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T10': 'StackerEnsembleModel_LGB',
'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
'model_performance': {'LightGBM_BAG_L1\\T1': -48.12131286915449,
'LightGBM_BAG_L1\\T2': -48.85590120876569,
'LightGBM_BAG_L1\\T3': -41.46354388112384,
'LightGBM_BAG_L1\\T4': -140.41943976016316,
'LightGBM_BAG_L1\\T5': -52.13384437355743,
'LightGBM_BAG_L1\\T6': -123.47521008865463,
'LightGBM_BAG_L1\\T7': -48.735403708143814,
'LightGBM_BAG_L1\\T8': -37.95289071380219,
'LightGBM_BAG_L1\\T9': -131.3383358294307,
'LightGBM_BAG_L1\\T10': -39.816719373139044,
'WeightedEnsemble_L2': -37.541252816500524,
'LightGBM_BAG_L2\\T1': -37.28389313469878,
'LightGBM_BAG_L2\\T2': -36.97194684554864,
'LightGBM_BAG_L2\\T3': -37.01245930973991,
'LightGBM_BAG_L2\\T4': -115.20345324732774,
'LightGBM_BAG_L2\\T5': -38.616601100504866,
'LightGBM_BAG_L2\\T6': -111.80662861630574,
'LightGBM_BAG_L2\\T7': -36.9636959240076,
'LightGBM_BAG_L2\\T8': -36.59621044633373,
'LightGBM_BAG_L2\\T9': -103.20973745528875,
'LightGBM_BAG_L2\\T10': -36.71720837197882,
'WeightedEnsemble_L3': -36.42831329448963},
'model_best': 'WeightedEnsemble_L3',
'model_paths': {'LightGBM_BAG_L1\\T1': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L1\\T1\\',
'LightGBM_BAG_L1\\T2': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L1\\T2\\',
'LightGBM_BAG_L1\\T3': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L1\\T3\\',
'LightGBM_BAG_L1\\T4': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L1\\T4\\',
'LightGBM_BAG_L1\\T5': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L1\\T5\\',
'LightGBM_BAG_L1\\T6': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L1\\T6\\',
'LightGBM_BAG_L1\\T7': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L1\\T7\\',
'LightGBM_BAG_L1\\T8': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L1\\T8\\',
'LightGBM_BAG_L1\\T9': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L1\\T9\\',
'LightGBM_BAG_L1\\T10': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L1\\T10\\',
'WeightedEnsemble_L2': 'AutogluonModels\\ag-20230531_025623\\models\\WeightedEnsemble_L2\\',
'LightGBM_BAG_L2\\T1': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L2\\T1\\',
'LightGBM_BAG_L2\\T2': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L2\\T2\\',
'LightGBM_BAG_L2\\T3': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L2\\T3\\',
'LightGBM_BAG_L2\\T4': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L2\\T4\\',
'LightGBM_BAG_L2\\T5': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L2\\T5\\',
'LightGBM_BAG_L2\\T6': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L2\\T6\\',
'LightGBM_BAG_L2\\T7': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L2\\T7\\',
'LightGBM_BAG_L2\\T8': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L2\\T8\\',
'LightGBM_BAG_L2\\T9': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L2\\T9\\',
'LightGBM_BAG_L2\\T10': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_025623\\models\\LightGBM_BAG_L2\\T10\\',
'WeightedEnsemble_L3': 'AutogluonModels\\ag-20230531_025623\\models\\WeightedEnsemble_L3\\'},
'model_fit_times': {'LightGBM_BAG_L1\\T1': 10.226464986801147,
'LightGBM_BAG_L1\\T2': 11.310179948806763,
'LightGBM_BAG_L1\\T3': 11.409003496170044,
'LightGBM_BAG_L1\\T4': 11.239344120025635,
'LightGBM_BAG_L1\\T5': 11.518471717834473,
'LightGBM_BAG_L1\\T6': 11.351787090301514,
'LightGBM_BAG_L1\\T7': 11.016189098358154,
'LightGBM_BAG_L1\\T8': 11.21375584602356,
'LightGBM_BAG_L1\\T9': 11.133766412734985,
'LightGBM_BAG_L1\\T10': 11.268539667129517,
'WeightedEnsemble_L2': 0.32626986503601074,
'LightGBM_BAG_L2\\T1': 11.083534002304077,
'LightGBM_BAG_L2\\T2': 11.67599868774414,
'LightGBM_BAG_L2\\T3': 11.559000253677368,
'LightGBM_BAG_L2\\T4': 11.196212768554688,
'LightGBM_BAG_L2\\T5': 11.367225170135498,
'LightGBM_BAG_L2\\T6': 11.07998776435852,
'LightGBM_BAG_L2\\T7': 11.014830827713013,
'LightGBM_BAG_L2\\T8': 11.239581823348999,
'LightGBM_BAG_L2\\T9': 11.06741738319397,
'LightGBM_BAG_L2\\T10': 11.177979946136475,
'WeightedEnsemble_L3': 0.30565690994262695},
'model_pred_times': {'LightGBM_BAG_L1\\T1': 0.0,
'LightGBM_BAG_L1\\T2': 0.0,
'LightGBM_BAG_L1\\T3': 0.0,
'LightGBM_BAG_L1\\T4': 0.0010008811950683594,
'LightGBM_BAG_L1\\T5': 0.0,
'LightGBM_BAG_L1\\T6': 0.0,
'LightGBM_BAG_L1\\T7': 0.0,
'LightGBM_BAG_L1\\T8': 0.0,
'LightGBM_BAG_L1\\T9': 0.0,
'LightGBM_BAG_L1\\T10': 0.0,
'WeightedEnsemble_L2': 0.0,
'LightGBM_BAG_L2\\T1': 0.0,
'LightGBM_BAG_L2\\T2': 0.0,
'LightGBM_BAG_L2\\T3': 0.0,
'LightGBM_BAG_L2\\T4': 0.0,
'LightGBM_BAG_L2\\T5': 0.0,
'LightGBM_BAG_L2\\T6': 0.0,
'LightGBM_BAG_L2\\T7': 0.0,
'LightGBM_BAG_L2\\T8': 0.0,
'LightGBM_BAG_L2\\T9': 0.0,
'LightGBM_BAG_L2\\T10': 0.0,
'WeightedEnsemble_L3': 0.0},
'num_bag_folds': 8,
'max_stack_level': 3,
'model_hyperparams': {'LightGBM_BAG_L1\\T1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T3': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T4': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T5': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T6': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T7': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T8': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T9': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T10': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L2': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T3': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T4': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T5': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T6': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T7': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T8': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T9': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T10': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L3': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True}},
'leaderboard': model score_val pred_time_val fit_time \
0 WeightedEnsemble_L3 -36.428313 0.001001 146.086720
1 LightGBM_BAG_L2\T8 -36.596210 0.001001 122.927084
2 LightGBM_BAG_L2\T10 -36.717208 0.001001 122.865482
3 LightGBM_BAG_L2\T7 -36.963696 0.001001 122.702333
4 LightGBM_BAG_L2\T2 -36.971947 0.001001 123.363501
5 LightGBM_BAG_L2\T3 -37.012459 0.001001 123.246503
6 LightGBM_BAG_L2\T1 -37.283893 0.001001 122.771036
7 WeightedEnsemble_L2 -37.541253 0.000000 22.808565
8 LightGBM_BAG_L1\T8 -37.952891 0.000000 11.213756
9 LightGBM_BAG_L2\T5 -38.616601 0.001001 123.054728
10 LightGBM_BAG_L1\T10 -39.816719 0.000000 11.268540
11 LightGBM_BAG_L1\T3 -41.463544 0.000000 11.409003
12 LightGBM_BAG_L1\T1 -48.121313 0.000000 10.226465
13 LightGBM_BAG_L1\T7 -48.735404 0.000000 11.016189
14 LightGBM_BAG_L1\T2 -48.855901 0.000000 11.310180
15 LightGBM_BAG_L1\T5 -52.133844 0.000000 11.518472
16 LightGBM_BAG_L2\T9 -103.209737 0.001001 122.754920
17 LightGBM_BAG_L2\T6 -111.806629 0.001001 122.767490
18 LightGBM_BAG_L2\T4 -115.203453 0.001001 122.883715
19 LightGBM_BAG_L1\T6 -123.475210 0.000000 11.351787
20 LightGBM_BAG_L1\T9 -131.338336 0.000000 11.133766
21 LightGBM_BAG_L1\T4 -140.419440 0.001001 11.239344
pred_time_val_marginal fit_time_marginal stack_level can_infer \
0 0.000000 0.305657 3 True
1 0.000000 11.239582 2 True
2 0.000000 11.177980 2 True
3 0.000000 11.014831 2 True
4 0.000000 11.675999 2 True
5 0.000000 11.559000 2 True
6 0.000000 11.083534 2 True
7 0.000000 0.326270 2 True
8 0.000000 11.213756 1 True
9 0.000000 11.367225 2 True
10 0.000000 11.268540 1 True
11 0.000000 11.409003 1 True
12 0.000000 10.226465 1 True
13 0.000000 11.016189 1 True
14 0.000000 11.310180 1 True
15 0.000000 11.518472 1 True
16 0.000000 11.067417 2 True
17 0.000000 11.079988 2 True
18 0.000000 11.196213 2 True
19 0.000000 11.351787 1 True
20 0.000000 11.133766 1 True
21 0.001001 11.239344 1 True
fit_order
0 22
1 19
2 21
3 18
4 13
5 14
6 12
7 11
8 8
9 16
10 10
11 3
12 1
13 7
14 2
15 5
16 20
17 17
18 15
19 6
20 9
21 4 }
predictions_new_features_hpo1 = predictor_new_hpo1.predict(test,model=predictor_new_hpo1.get_model_best())
predictions_new_features_hpo1.describe()
count 6493.000000 mean 190.777222 std 174.822235 min 4.802301 25% 46.602318 50% 148.788101 75% 284.207275 max 875.185486 Name: count, dtype: float64
predictions_new_features_hpo1[predictions_new_features_hpo1<0].count()
0
predictions_new_features_hpo1[predictions_new_features_hpo1<0]=0
submission_new_hpo1 = submission.copy(deep=True)
# Same submitting predictions
submission_new_hpo1["count"] = predictions_new_features_hpo1
submission_new_hpo1.to_csv("submission_new_hpo1.csv", index=False)
!kaggle competitions submit -c bike-sharing-demand -f submission_new_hpo1.csv -m "new features with hyperparameters1"
Successfully submitted to Bike Sharing Demand
0%| | 0.00/194k [00:00<?, ?B/s] 4%|4 | 8.00k/194k [00:00<00:05, 37.0kB/s] 95%|#########4| 184k/194k [00:00<00:00, 726kB/s] 100%|##########| 194k/194k [00:04<00:00, 41.4kB/s]
!kaggle competitions submissions -c bike-sharing-demand
#| tail -n +1 | head -n 6
fileName date description status publicScore privateScore --------------------------- ------------------- ---------------------------------- -------- ----------- ------------ submission_new_hpo1.csv 2023-05-31 03:08:31 new features with hyperparameters1 complete 0.46803 0.46803 submission_new_hpo.csv 2023-05-31 01:32:59 complete 0.49844 0.49844 submission_new_hpo.csv 2023-05-31 00:59:18 new features with hyperparameters complete 0.49844 0.49844 submission_new_features.csv 2023-05-30 06:32:08 new features complete 0.70588 0.70588 submission.csv 2023-05-30 05:49:46 first raw submission complete 1.80425 1.80425
nn_options = { # specifies non-default hyperparameter values for neural network models
'num_epochs': ag.space.Int(lower=8, upper=20, default=10), # number of training epochs (controls training time of NN models)
'learning_rate': ag.space.Real(1e-3, 1e-2, default=5e-3, log=True), # learning rate used in training (real-valued hyperparameter searched on log-scale)
'activation': ag.space.Categorical('relu', 'softrelu', 'tanh'), # activation function used in NN (categorical hyperparameter, default = first entry)
'layers': ag.space.Categorical([100],[1000],[200, 100]), # each choice for categorical hyperparameter 'layers' corresponds to list of sizes for each NN layer to use
'dropout_prob': ag.space.Real(0.0, 0.5, default=0.1), # dropout probability (real-valued hyperparameter)
}
gbm_options = { # specifies non-default hyperparameter values for lightGBM gradient boosted trees
'num_boost_round': 80, # number of boosting rounds (controls training time of GBM models)
'num_leaves': ag.space.Int(lower=10, upper=50, default=20), # number of leaves in trees (integer hyperparameter)
}
hyperparameters = { # hyperparameters of each model type
'GBM': gbm_options,
'NN_TORCH': nn_options,
}
time_limit = 10*60 # train various models for ~2 min
num_trials = 10 # try at most 5 different hyperparameter configurations for each type of model
search_strategy = 'auto' # to tune hyperparameters using Bayesian optimization routine with a local scheduler
hyperparameter_tune_kwargs = {
'num_trials': num_trials,
'scheduler' : 'local',
'searcher': search_strategy,
}
predictor_new_hpo2 = TabularPredictor('count',problem_type='regression',learner_kwargs={"ignored_columns":['casual','registered']},
eval_metric='root_mean_squared_error').fit(train,time_limit=time_limit,presets='best_quality',
hyperparameters=hyperparameters,
hyperparameter_tune_kwargs=hyperparameter_tune_kwargs)
No path specified. Models will be saved in: "AutogluonModels\ag-20230531_043337\"
Presets specified: ['best_quality']
Warning: hyperparameter tuning is currently experimental and may cause the process to hang.
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels\ag-20230531_043337\"
AutoGluon Version: 0.7.0
Python Version: 3.8.16
Operating System: Windows
Platform Machine: AMD64
Platform Version: 10.0.19045
Train Data Rows: 10886
Train Data Columns: 15
Label Column: count
Preprocessing data ...
Using Feature Generators to preprocess the data ...
Dropping user-specified ignored columns: ['casual', 'registered']
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 6915.38 MB
Train Data (Original) Memory Usage: 0.83 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 3 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Fitting DatetimeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('category', []) : 4 | ['season', 'weather', 'year', 'weekday']
('datetime', []) : 1 | ['datetime']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 5 | ['holiday', 'workingday', 'humidity', 'week', 'hour']
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 3 | ['season', 'weather', 'weekday']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['humidity', 'week', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
0.1s = Fit runtime
13 features in original data used to generate 17 features in processed data.
Train Data (Processed) Memory Usage: 1.02 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.17s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
To change this, specify the eval_metric parameter of Predictor()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 2 L1 models ...
Hyperparameter tuning model: LightGBM_BAG_L1 ... Tuning model for up to 179.9s of the 599.83s of remaining time.
0%| | 0/10 [00:00<?, ?it/s]
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitted model: LightGBM_BAG_L1\T1 ...
-48.1213 = Validation score (-root_mean_squared_error)
16.37s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1\T2 ...
-48.8559 = Validation score (-root_mean_squared_error)
11.35s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1\T3 ...
-41.4635 = Validation score (-root_mean_squared_error)
11.06s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1\T4 ...
-140.4194 = Validation score (-root_mean_squared_error)
10.92s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1\T5 ...
-52.1338 = Validation score (-root_mean_squared_error)
11.05s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1\T6 ...
-123.4752 = Validation score (-root_mean_squared_error)
11.05s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1\T7 ...
-48.7354 = Validation score (-root_mean_squared_error)
11.04s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1\T8 ...
-37.9529 = Validation score (-root_mean_squared_error)
10.99s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1\T9 ...
-131.3383 = Validation score (-root_mean_squared_error)
10.87s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L1\T10 ...
-39.8167 = Validation score (-root_mean_squared_error)
10.85s = Training runtime
0.0s = Validation runtime
Hyperparameter tuning model: NeuralNetTorch_BAG_L1 ... Tuning model for up to 179.9s of the 482.89s of remaining time.
Warning: Exception caused NeuralNetTorch_BAG_L1 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\hyperopt\hyperopt_search.py", line 252, in _lookup
idx = categories.index(config_dict[key])
ValueError: [100] is not in list
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\tuner.py", line 272, in fit
return self._local_tuner.fit()
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\impl\tuner_internal.py", line 420, in fit
analysis = self._fit_internal(trainable, param_space)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\impl\tuner_internal.py", line 532, in _fit_internal
analysis = run(
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\tune.py", line 597, in run
if config and not searcher_set_search_props(
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\util.py", line 19, in _set_search_properties_backwards_compatible
return set_search_properties_func(metric, mode, config, **spec)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\search_generator.py", line 63, in set_search_properties
return _set_search_properties_backwards_compatible(
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\util.py", line 19, in _set_search_properties_backwards_compatible
return set_search_properties_func(metric, mode, config, **spec)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\hyperopt\hyperopt_search.py", line 292, in set_search_properties
self._setup_hyperopt()
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\hyperopt\hyperopt_search.py", line 222, in _setup_hyperopt
self._convert_categories_to_indices(config)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\hyperopt\hyperopt_search.py", line 275, in _convert_categories_to_indices
_lookup(config, self._space, k)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\hyperopt\hyperopt_search.py", line 271, in _lookup
raise ValueError(msg) from exc
ValueError: Did not find category with value `[100]` in hyperopt parameter `layers`. Please make sure the specified category is valid.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\trainer\abstract_trainer.py", line 1761, in _train_single_full
hpo_models, hpo_results = model.hyperparameter_tune(
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 1257, in hyperparameter_tune
return self._hyperparameter_tune(hpo_executor=hpo_executor, **kwargs)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 182, in _hyperparameter_tune
return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, hpo_executor=hpo_executor, preprocess_kwargs=preprocess_kwargs, **kwargs)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 1177, in _hyperparameter_tune
hpo_executor.execute(
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\hpo\executors.py", line 375, in execute
analysis = run(
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\hpo\ray_hpo.py", line 292, in run
results = tuner.fit()
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\tuner.py", line 274, in fit
raise TuneError(
ray.tune.error.TuneError: The Ray Tune run failed. Please inspect the previous error messages for a cause. After fixing the issue, you can restart the run from scratch or continue this run. To continue this run, you can use `tuner = Tuner.restore("E:\online competition\kaggle\udacity_nd_bike_sharing_project\cd0385-project-starter\project\AutogluonModels\ag-20230531_043337\models\NeuralNetTorch_BAG_L1")`.
The Ray Tune run failed. Please inspect the previous error messages for a cause. After fixing the issue, you can restart the run from scratch or continue this run. To continue this run, you can use `tuner = Tuner.restore("E:\online competition\kaggle\udacity_nd_bike_sharing_project\cd0385-project-starter\project\AutogluonModels\ag-20230531_043337\models\NeuralNetTorch_BAG_L1")`.
Repeating k-fold bagging: 2/20
Fitting model: LightGBM_BAG_L1\T1 ... Training model for up to 282.4s of the 482.45s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-47.8427 = Validation score (-root_mean_squared_error)
17.96s = Training runtime
0.1s = Validation runtime
Fitting model: LightGBM_BAG_L1\T2 ... Training model for up to 271.51s of the 471.55s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-48.4642 = Validation score (-root_mean_squared_error)
12.95s = Training runtime
0.08s = Validation runtime
Fitting model: LightGBM_BAG_L1\T3 ... Training model for up to 260.6s of the 460.65s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-41.0288 = Validation score (-root_mean_squared_error)
12.7s = Training runtime
0.11s = Validation runtime
Fitting model: LightGBM_BAG_L1\T4 ... Training model for up to 249.14s of the 449.19s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-140.404 = Validation score (-root_mean_squared_error)
12.44s = Training runtime
0.06s = Validation runtime
Fitting model: LightGBM_BAG_L1\T5 ... Training model for up to 238.41s of the 438.45s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-51.9554 = Validation score (-root_mean_squared_error)
12.65s = Training runtime
0.09s = Validation runtime
Fitting model: LightGBM_BAG_L1\T6 ... Training model for up to 227.63s of the 427.68s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-123.3835 = Validation score (-root_mean_squared_error)
12.65s = Training runtime
0.08s = Validation runtime
Fitting model: LightGBM_BAG_L1\T7 ... Training model for up to 216.77s of the 416.82s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-48.3041 = Validation score (-root_mean_squared_error)
12.58s = Training runtime
0.07s = Validation runtime
Fitting model: LightGBM_BAG_L1\T8 ... Training model for up to 206.06s of the 406.11s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-37.2628 = Validation score (-root_mean_squared_error)
12.63s = Training runtime
0.12s = Validation runtime
Fitting model: LightGBM_BAG_L1\T9 ... Training model for up to 195.12s of the 395.16s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-131.3088 = Validation score (-root_mean_squared_error)
12.39s = Training runtime
0.06s = Validation runtime
Fitting model: LightGBM_BAG_L1\T10 ... Training model for up to 184.29s of the 384.34s of remaining time.
Fitting 8 child models (S2F1 - S2F8) | Fitting with ParallelLocalFoldFittingStrategy
-38.9148 = Validation score (-root_mean_squared_error)
12.38s = Training runtime
0.09s = Validation runtime
Repeating k-fold bagging: 3/20
Fitting model: LightGBM_BAG_L1\T1 ... Training model for up to 173.49s of the 373.53s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-47.6504 = Validation score (-root_mean_squared_error)
19.62s = Training runtime
0.21s = Validation runtime
Fitting model: LightGBM_BAG_L1\T2 ... Training model for up to 162.54s of the 362.59s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-48.2846 = Validation score (-root_mean_squared_error)
14.51s = Training runtime
0.16s = Validation runtime
Fitting model: LightGBM_BAG_L1\T3 ... Training model for up to 151.71s of the 351.75s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-40.8692 = Validation score (-root_mean_squared_error)
14.36s = Training runtime
0.23s = Validation runtime
Fitting model: LightGBM_BAG_L1\T4 ... Training model for up to 140.59s of the 340.64s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-140.3918 = Validation score (-root_mean_squared_error)
13.98s = Training runtime
0.14s = Validation runtime
Fitting model: LightGBM_BAG_L1\T5 ... Training model for up to 129.63s of the 329.67s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-51.8047 = Validation score (-root_mean_squared_error)
14.27s = Training runtime
0.17s = Validation runtime
Fitting model: LightGBM_BAG_L1\T6 ... Training model for up to 118.66s of the 318.7s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-123.4192 = Validation score (-root_mean_squared_error)
14.31s = Training runtime
0.16s = Validation runtime
Fitting model: LightGBM_BAG_L1\T7 ... Training model for up to 107.79s of the 307.83s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-48.112 = Validation score (-root_mean_squared_error)
14.13s = Training runtime
0.15s = Validation runtime
Fitting model: LightGBM_BAG_L1\T8 ... Training model for up to 96.7s of the 296.74s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-37.024 = Validation score (-root_mean_squared_error)
14.23s = Training runtime
0.24s = Validation runtime
Fitting model: LightGBM_BAG_L1\T9 ... Training model for up to 85.84s of the 285.88s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-131.313 = Validation score (-root_mean_squared_error)
14.02s = Training runtime
0.13s = Validation runtime
Fitting model: LightGBM_BAG_L1\T10 ... Training model for up to 74.91s of the 274.95s of remaining time.
Fitting 8 child models (S3F1 - S3F8) | Fitting with ParallelLocalFoldFittingStrategy
-38.6076 = Validation score (-root_mean_squared_error)
13.96s = Training runtime
0.18s = Validation runtime
Completed 3/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 263.65s of remaining time.
-36.7956 = Validation score (-root_mean_squared_error)
0.23s = Training runtime
0.0s = Validation runtime
Fitting 2 L2 models ...
Hyperparameter tuning model: LightGBM_BAG_L2 ... Tuning model for up to 118.53s of the 263.39s of remaining time.
0%| | 0/10 [00:00<?, ?it/s]
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
Stopping HPO to satisfy time limit...
Fitted model: LightGBM_BAG_L2\T1 ...
-36.4404 = Validation score (-root_mean_squared_error)
11.3s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2\T2 ...
-36.172 = Validation score (-root_mean_squared_error)
11.34s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2\T3 ...
-36.4068 = Validation score (-root_mean_squared_error)
11.44s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2\T4 ...
-115.0644 = Validation score (-root_mean_squared_error)
10.9s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2\T5 ...
-37.7837 = Validation score (-root_mean_squared_error)
11.46s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2\T6 ...
-111.6776 = Validation score (-root_mean_squared_error)
11.1s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2\T7 ...
-36.2864 = Validation score (-root_mean_squared_error)
11.29s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2\T8 ...
-36.0603 = Validation score (-root_mean_squared_error)
11.13s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2\T9 ...
-103.007 = Validation score (-root_mean_squared_error)
10.77s = Training runtime
0.0s = Validation runtime
Fitted model: LightGBM_BAG_L2\T10 ...
-35.9819 = Validation score (-root_mean_squared_error)
11.11s = Training runtime
0.0s = Validation runtime
Hyperparameter tuning model: NeuralNetTorch_BAG_L2 ... Tuning model for up to 118.53s of the 151.32s of remaining time.
Warning: Exception caused NeuralNetTorch_BAG_L2 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\hyperopt\hyperopt_search.py", line 252, in _lookup
idx = categories.index(config_dict[key])
ValueError: [100] is not in list
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\tuner.py", line 272, in fit
return self._local_tuner.fit()
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\impl\tuner_internal.py", line 420, in fit
analysis = self._fit_internal(trainable, param_space)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\impl\tuner_internal.py", line 532, in _fit_internal
analysis = run(
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\tune.py", line 597, in run
if config and not searcher_set_search_props(
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\util.py", line 19, in _set_search_properties_backwards_compatible
return set_search_properties_func(metric, mode, config, **spec)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\search_generator.py", line 63, in set_search_properties
return _set_search_properties_backwards_compatible(
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\util.py", line 19, in _set_search_properties_backwards_compatible
return set_search_properties_func(metric, mode, config, **spec)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\hyperopt\hyperopt_search.py", line 292, in set_search_properties
self._setup_hyperopt()
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\hyperopt\hyperopt_search.py", line 222, in _setup_hyperopt
self._convert_categories_to_indices(config)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\hyperopt\hyperopt_search.py", line 275, in _convert_categories_to_indices
_lookup(config, self._space, k)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\search\hyperopt\hyperopt_search.py", line 271, in _lookup
raise ValueError(msg) from exc
ValueError: Did not find category with value `[100]` in hyperopt parameter `layers`. Please make sure the specified category is valid.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\trainer\abstract_trainer.py", line 1761, in _train_single_full
hpo_models, hpo_results = model.hyperparameter_tune(
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 1257, in hyperparameter_tune
return self._hyperparameter_tune(hpo_executor=hpo_executor, **kwargs)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 182, in _hyperparameter_tune
return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, hpo_executor=hpo_executor, preprocess_kwargs=preprocess_kwargs, **kwargs)
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 1177, in _hyperparameter_tune
hpo_executor.execute(
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\hpo\executors.py", line 375, in execute
analysis = run(
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\autogluon\core\hpo\ray_hpo.py", line 292, in run
results = tuner.fit()
File "C:\Users\Chris\anaconda3\envs\py38_base\lib\site-packages\ray\tune\tuner.py", line 274, in fit
raise TuneError(
ray.tune.error.TuneError: The Ray Tune run failed. Please inspect the previous error messages for a cause. After fixing the issue, you can restart the run from scratch or continue this run. To continue this run, you can use `tuner = Tuner.restore("E:\online competition\kaggle\udacity_nd_bike_sharing_project\cd0385-project-starter\project\AutogluonModels\ag-20230531_043337\models\NeuralNetTorch_BAG_L2")`.
The Ray Tune run failed. Please inspect the previous error messages for a cause. After fixing the issue, you can restart the run from scratch or continue this run. To continue this run, you can use `tuner = Tuner.restore("E:\online competition\kaggle\udacity_nd_bike_sharing_project\cd0385-project-starter\project\AutogluonModels\ag-20230531_043337\models\NeuralNetTorch_BAG_L2")`.
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 151.22s of remaining time.
-35.7741 = Validation score (-root_mean_squared_error)
0.26s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 449.07s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels\ag-20230531_043337\")
predictor_new_hpo2.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L3 -35.774051 1.772058 181.241197 0.000999 0.263998 3 True 22
1 LightGBM_BAG_L2\T10 -35.981921 1.771059 158.507892 0.001000 11.113278 2 True 21
2 LightGBM_BAG_L2\T8 -36.060335 1.770059 158.524888 0.000000 11.130274 2 True 19
3 LightGBM_BAG_L2\T2 -36.171994 1.770059 158.733648 0.000000 11.339034 2 True 13
4 LightGBM_BAG_L2\T7 -36.286430 1.770059 158.686630 0.000000 11.292016 2 True 18
5 LightGBM_BAG_L2\T3 -36.406820 1.770059 158.835299 0.000000 11.440685 2 True 14
6 LightGBM_BAG_L2\T1 -36.440448 1.770059 158.692754 0.000000 11.298140 2 True 12
7 WeightedEnsemble_L2 -36.795580 0.418004 28.422191 0.001000 0.230996 2 True 11
8 LightGBM_BAG_L1\T8 -37.024016 0.236002 14.231187 0.236002 14.231187 1 True 8
9 LightGBM_BAG_L2\T5 -37.783677 1.770059 158.849647 0.000000 11.455033 2 True 16
10 LightGBM_BAG_L1\T10 -38.607579 0.181001 13.960008 0.181001 13.960008 1 True 10
11 LightGBM_BAG_L1\T3 -40.869191 0.234013 14.358388 0.234013 14.358388 1 True 3
12 LightGBM_BAG_L1\T1 -47.650433 0.205012 19.618656 0.205012 19.618656 1 True 1
13 LightGBM_BAG_L1\T7 -48.111992 0.153008 14.131077 0.153008 14.131077 1 True 7
14 LightGBM_BAG_L1\T2 -48.284649 0.161995 14.505001 0.161995 14.505001 1 True 2
15 LightGBM_BAG_L1\T5 -51.804726 0.172031 14.274050 0.172031 14.274050 1 True 5
16 LightGBM_BAG_L2\T9 -103.007016 1.770059 158.161762 0.000000 10.767148 2 True 20
17 LightGBM_BAG_L2\T6 -111.677585 1.770059 158.498611 0.000000 11.103997 2 True 17
18 LightGBM_BAG_L2\T4 -115.064413 1.770059 158.293782 0.000000 10.899168 2 True 15
19 LightGBM_BAG_L1\T6 -123.419154 0.162997 14.314400 0.162997 14.314400 1 True 6
20 LightGBM_BAG_L1\T9 -131.312991 0.126005 14.018013 0.126005 14.018013 1 True 9
21 LightGBM_BAG_L1\T4 -140.391777 0.137994 13.983833 0.137994 13.983833 1 True 4
Number of models trained: 22
Types of models trained:
{'WeightedEnsembleModel', 'StackerEnsembleModel_LGB'}
Bagging used: True (with 8 folds)
Multi-layer stack-ensembling used: True (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', []) : 3 | ['season', 'weather', 'weekday']
('float', []) : 3 | ['temp', 'atemp', 'windspeed']
('int', []) : 3 | ['humidity', 'week', 'hour']
('int', ['bool']) : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
*** End of fit() summary ***
{'model_types': {'LightGBM_BAG_L1\\T1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T3': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T4': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T5': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T6': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T7': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T8': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T9': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L1\\T10': 'StackerEnsembleModel_LGB',
'WeightedEnsemble_L2': 'WeightedEnsembleModel',
'LightGBM_BAG_L2\\T1': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T2': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T3': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T4': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T5': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T6': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T7': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T8': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T9': 'StackerEnsembleModel_LGB',
'LightGBM_BAG_L2\\T10': 'StackerEnsembleModel_LGB',
'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
'model_performance': {'LightGBM_BAG_L1\\T1': -47.650432794522196,
'LightGBM_BAG_L1\\T2': -48.28464931909125,
'LightGBM_BAG_L1\\T3': -40.86919096387156,
'LightGBM_BAG_L1\\T4': -140.39177699464057,
'LightGBM_BAG_L1\\T5': -51.80472626835246,
'LightGBM_BAG_L1\\T6': -123.41915363718512,
'LightGBM_BAG_L1\\T7': -48.111991913771384,
'LightGBM_BAG_L1\\T8': -37.02401631024006,
'LightGBM_BAG_L1\\T9': -131.31299067162985,
'LightGBM_BAG_L1\\T10': -38.607578808884156,
'WeightedEnsemble_L2': -36.79558000753171,
'LightGBM_BAG_L2\\T1': -36.440448487671716,
'LightGBM_BAG_L2\\T2': -36.17199449477448,
'LightGBM_BAG_L2\\T3': -36.40682028374784,
'LightGBM_BAG_L2\\T4': -115.06441262162491,
'LightGBM_BAG_L2\\T5': -37.78367715761663,
'LightGBM_BAG_L2\\T6': -111.6775845548824,
'LightGBM_BAG_L2\\T7': -36.28643017794523,
'LightGBM_BAG_L2\\T8': -36.06033513523615,
'LightGBM_BAG_L2\\T9': -103.00701583912745,
'LightGBM_BAG_L2\\T10': -35.98192125320506,
'WeightedEnsemble_L3': -35.774050913898265},
'model_best': 'WeightedEnsemble_L3',
'model_paths': {'LightGBM_BAG_L1\\T1': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L1\\T1\\',
'LightGBM_BAG_L1\\T2': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L1\\T2\\',
'LightGBM_BAG_L1\\T3': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L1\\T3\\',
'LightGBM_BAG_L1\\T4': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L1\\T4\\',
'LightGBM_BAG_L1\\T5': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L1\\T5\\',
'LightGBM_BAG_L1\\T6': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L1\\T6\\',
'LightGBM_BAG_L1\\T7': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L1\\T7\\',
'LightGBM_BAG_L1\\T8': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L1\\T8\\',
'LightGBM_BAG_L1\\T9': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L1\\T9\\',
'LightGBM_BAG_L1\\T10': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L1\\T10\\',
'WeightedEnsemble_L2': 'AutogluonModels\\ag-20230531_043337\\models\\WeightedEnsemble_L2\\',
'LightGBM_BAG_L2\\T1': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L2\\T1\\',
'LightGBM_BAG_L2\\T2': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L2\\T2\\',
'LightGBM_BAG_L2\\T3': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L2\\T3\\',
'LightGBM_BAG_L2\\T4': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L2\\T4\\',
'LightGBM_BAG_L2\\T5': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L2\\T5\\',
'LightGBM_BAG_L2\\T6': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L2\\T6\\',
'LightGBM_BAG_L2\\T7': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L2\\T7\\',
'LightGBM_BAG_L2\\T8': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L2\\T8\\',
'LightGBM_BAG_L2\\T9': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L2\\T9\\',
'LightGBM_BAG_L2\\T10': 'E:\\online competition\\kaggle\\udacity_nd_bike_sharing_project\\cd0385-project-starter\\project\\AutogluonModels\\ag-20230531_043337\\models\\LightGBM_BAG_L2\\T10\\',
'WeightedEnsemble_L3': 'AutogluonModels\\ag-20230531_043337\\models\\WeightedEnsemble_L3\\'},
'model_fit_times': {'LightGBM_BAG_L1\\T1': 19.618656158447266,
'LightGBM_BAG_L1\\T2': 14.505001068115234,
'LightGBM_BAG_L1\\T3': 14.358388423919678,
'LightGBM_BAG_L1\\T4': 13.983833312988281,
'LightGBM_BAG_L1\\T5': 14.274049997329712,
'LightGBM_BAG_L1\\T6': 14.314399719238281,
'LightGBM_BAG_L1\\T7': 14.131077289581299,
'LightGBM_BAG_L1\\T8': 14.231187105178833,
'LightGBM_BAG_L1\\T9': 14.018013000488281,
'LightGBM_BAG_L1\\T10': 13.960007905960083,
'WeightedEnsemble_L2': 0.23099565505981445,
'LightGBM_BAG_L2\\T1': 11.298139810562134,
'LightGBM_BAG_L2\\T2': 11.339033842086792,
'LightGBM_BAG_L2\\T3': 11.44068455696106,
'LightGBM_BAG_L2\\T4': 10.899168252944946,
'LightGBM_BAG_L2\\T5': 11.455033302307129,
'LightGBM_BAG_L2\\T6': 11.103997230529785,
'LightGBM_BAG_L2\\T7': 11.292015552520752,
'LightGBM_BAG_L2\\T8': 11.130273580551147,
'LightGBM_BAG_L2\\T9': 10.767148494720459,
'LightGBM_BAG_L2\\T10': 11.113278150558472,
'WeightedEnsemble_L3': 0.26399779319763184},
'model_pred_times': {'LightGBM_BAG_L1\\T1': 0.20501208305358887,
'LightGBM_BAG_L1\\T2': 0.16199493408203125,
'LightGBM_BAG_L1\\T3': 0.23401331901550293,
'LightGBM_BAG_L1\\T4': 0.13799357414245605,
'LightGBM_BAG_L1\\T5': 0.17203116416931152,
'LightGBM_BAG_L1\\T6': 0.16299748420715332,
'LightGBM_BAG_L1\\T7': 0.15300798416137695,
'LightGBM_BAG_L1\\T8': 0.23600172996520996,
'LightGBM_BAG_L1\\T9': 0.12600493431091309,
'LightGBM_BAG_L1\\T10': 0.1810014247894287,
'WeightedEnsemble_L2': 0.0010004043579101562,
'LightGBM_BAG_L2\\T1': 0.0,
'LightGBM_BAG_L2\\T2': 0.0,
'LightGBM_BAG_L2\\T3': 0.0,
'LightGBM_BAG_L2\\T4': 0.0,
'LightGBM_BAG_L2\\T5': 0.0,
'LightGBM_BAG_L2\\T6': 0.0,
'LightGBM_BAG_L2\\T7': 0.0,
'LightGBM_BAG_L2\\T8': 0.0,
'LightGBM_BAG_L2\\T9': 0.0,
'LightGBM_BAG_L2\\T10': 0.0010001659393310547,
'WeightedEnsemble_L3': 0.0009989738464355469},
'num_bag_folds': 8,
'max_stack_level': 3,
'model_hyperparams': {'LightGBM_BAG_L1\\T1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T3': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T4': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T5': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T6': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T7': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T8': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T9': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L1\\T10': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L2': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T1': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T2': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T3': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T4': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T5': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T6': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T7': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T8': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T9': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'LightGBM_BAG_L2\\T10': {'use_orig_features': True,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True},
'WeightedEnsemble_L3': {'use_orig_features': False,
'max_base_models': 25,
'max_base_models_per_type': 5,
'save_bag_folds': True}},
'leaderboard': model score_val pred_time_val fit_time \
0 WeightedEnsemble_L3 -35.774051 1.772058 181.241197
1 LightGBM_BAG_L2\T10 -35.981921 1.771059 158.507892
2 LightGBM_BAG_L2\T8 -36.060335 1.770059 158.524888
3 LightGBM_BAG_L2\T2 -36.171994 1.770059 158.733648
4 LightGBM_BAG_L2\T7 -36.286430 1.770059 158.686630
5 LightGBM_BAG_L2\T3 -36.406820 1.770059 158.835299
6 LightGBM_BAG_L2\T1 -36.440448 1.770059 158.692754
7 WeightedEnsemble_L2 -36.795580 0.418004 28.422191
8 LightGBM_BAG_L1\T8 -37.024016 0.236002 14.231187
9 LightGBM_BAG_L2\T5 -37.783677 1.770059 158.849647
10 LightGBM_BAG_L1\T10 -38.607579 0.181001 13.960008
11 LightGBM_BAG_L1\T3 -40.869191 0.234013 14.358388
12 LightGBM_BAG_L1\T1 -47.650433 0.205012 19.618656
13 LightGBM_BAG_L1\T7 -48.111992 0.153008 14.131077
14 LightGBM_BAG_L1\T2 -48.284649 0.161995 14.505001
15 LightGBM_BAG_L1\T5 -51.804726 0.172031 14.274050
16 LightGBM_BAG_L2\T9 -103.007016 1.770059 158.161762
17 LightGBM_BAG_L2\T6 -111.677585 1.770059 158.498611
18 LightGBM_BAG_L2\T4 -115.064413 1.770059 158.293782
19 LightGBM_BAG_L1\T6 -123.419154 0.162997 14.314400
20 LightGBM_BAG_L1\T9 -131.312991 0.126005 14.018013
21 LightGBM_BAG_L1\T4 -140.391777 0.137994 13.983833
pred_time_val_marginal fit_time_marginal stack_level can_infer \
0 0.000999 0.263998 3 True
1 0.001000 11.113278 2 True
2 0.000000 11.130274 2 True
3 0.000000 11.339034 2 True
4 0.000000 11.292016 2 True
5 0.000000 11.440685 2 True
6 0.000000 11.298140 2 True
7 0.001000 0.230996 2 True
8 0.236002 14.231187 1 True
9 0.000000 11.455033 2 True
10 0.181001 13.960008 1 True
11 0.234013 14.358388 1 True
12 0.205012 19.618656 1 True
13 0.153008 14.131077 1 True
14 0.161995 14.505001 1 True
15 0.172031 14.274050 1 True
16 0.000000 10.767148 2 True
17 0.000000 11.103997 2 True
18 0.000000 10.899168 2 True
19 0.162997 14.314400 1 True
20 0.126005 14.018013 1 True
21 0.137994 13.983833 1 True
fit_order
0 22
1 21
2 19
3 13
4 18
5 14
6 12
7 11
8 8
9 16
10 10
11 3
12 1
13 7
14 2
15 5
16 20
17 17
18 15
19 6
20 9
21 4 }
predictions_new_features_hpo2 = predictor_new_hpo2.predict(test,model=predictor_new_hpo2.get_model_best())
predictions_new_features_hpo2.head()
0 10.651279 1 6.327431 2 6.279728 3 6.315984 4 6.302880 Name: count, dtype: float32
# Remember to set all negative values to zero
predictions_new_features_hpo2.describe()
count 6493.000000 mean 190.663055 std 174.672501 min 5.210919 25% 43.650482 50% 148.237976 75% 283.945831 max 877.211304 Name: count, dtype: float64
predictions_new_features_hpo2[predictions_new_features_hpo2<0].count()
0
predictions_new_features_hpo2[predictions_new_features_hpo2<0]=0
submission_new_hpo2 = submission.copy(deep=True)
# Same submitting predictions
submission_new_hpo2["count"] = predictions_new_features_hpo2
submission_new_hpo2.to_csv("submission_new_hpo2.csv", index=False)
!kaggle competitions submit -c bike-sharing-demand -f submission_new_hpo2.csv -m "new features with hyperparameters2"
Successfully submitted to Bike Sharing Demand
0%| | 0.00/194k [00:00<?, ?B/s] 4%|4 | 8.00k/194k [00:00<00:05, 32.1kB/s] 95%|#########4| 184k/194k [00:00<00:00, 628kB/s] 100%|##########| 194k/194k [00:04<00:00, 40.8kB/s]
!kaggle competitions submissions -c bike-sharing-demand
#| tail -n +1 | head -n 6
fileName date description status publicScore privateScore --------------------------- ------------------- ---------------------------------- -------- ----------- ------------ submission_new_hpo2.csv 2023-05-31 04:42:10 new features with hyperparameters2 complete 0.46765 0.46765 submission_new_hpo1.csv 2023-05-31 03:08:31 new features with hyperparameters1 complete 0.46803 0.46803 submission_new_hpo.csv 2023-05-31 01:32:59 complete 0.49844 0.49844 submission_new_hpo.csv 2023-05-31 00:59:18 new features with hyperparameters complete 0.49844 0.49844 submission_new_features.csv 2023-05-30 06:32:08 new features complete 0.70588 0.70588 submission.csv 2023-05-30 05:49:46 first raw submission complete 1.80425 1.80425
baseline_leaderboard=predictor.leaderboard()
newfeatures_leaderboard=predictor_new_features.leaderboard()
hpo_leaderboard1=predictor_new_hpo.leaderboard()
hpo_leaderboard2=predictor_new_hpo1.leaderboard()
hpo_leaderboard3=predictor_new_hpo2.leaderboard()
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order 0 WeightedEnsemble_L3 -35.774051 1.772058 181.241197 0.000999 0.263998 3 True 22 1 LightGBM_BAG_L2\T10 -35.981921 1.771059 158.507892 0.001000 11.113278 2 True 21 2 LightGBM_BAG_L2\T8 -36.060335 1.770059 158.524888 0.000000 11.130274 2 True 19 3 LightGBM_BAG_L2\T2 -36.171994 1.770059 158.733648 0.000000 11.339034 2 True 13 4 LightGBM_BAG_L2\T7 -36.286430 1.770059 158.686630 0.000000 11.292016 2 True 18 5 LightGBM_BAG_L2\T3 -36.406820 1.770059 158.835299 0.000000 11.440685 2 True 14 6 LightGBM_BAG_L2\T1 -36.440448 1.770059 158.692754 0.000000 11.298140 2 True 12 7 WeightedEnsemble_L2 -36.795580 0.418004 28.422191 0.001000 0.230996 2 True 11 8 LightGBM_BAG_L1\T8 -37.024016 0.236002 14.231187 0.236002 14.231187 1 True 8 9 LightGBM_BAG_L2\T5 -37.783677 1.770059 158.849647 0.000000 11.455033 2 True 16 10 LightGBM_BAG_L1\T10 -38.607579 0.181001 13.960008 0.181001 13.960008 1 True 10 11 LightGBM_BAG_L1\T3 -40.869191 0.234013 14.358388 0.234013 14.358388 1 True 3 12 LightGBM_BAG_L1\T1 -47.650433 0.205012 19.618656 0.205012 19.618656 1 True 1 13 LightGBM_BAG_L1\T7 -48.111992 0.153008 14.131077 0.153008 14.131077 1 True 7 14 LightGBM_BAG_L1\T2 -48.284649 0.161995 14.505001 0.161995 14.505001 1 True 2 15 LightGBM_BAG_L1\T5 -51.804726 0.172031 14.274050 0.172031 14.274050 1 True 5 16 LightGBM_BAG_L2\T9 -103.007016 1.770059 158.161762 0.000000 10.767148 2 True 20 17 LightGBM_BAG_L2\T6 -111.677585 1.770059 158.498611 0.000000 11.103997 2 True 17 18 LightGBM_BAG_L2\T4 -115.064413 1.770059 158.293782 0.000000 10.899168 2 True 15 19 LightGBM_BAG_L1\T6 -123.419154 0.162997 14.314400 0.162997 14.314400 1 True 6 20 LightGBM_BAG_L1\T9 -131.312991 0.126005 14.018013 0.126005 14.018013 1 True 9 21 LightGBM_BAG_L1\T4 -140.391777 0.137994 13.983833 0.137994 13.983833 1 True 4
baseline_model_score = abs(baseline_leaderboard['score_val'][0]) #50.545607#
newfeatures_model_score = abs(newfeatures_leaderboard['score_val'][0]) #29.890078#
hpo_model_score1 = abs(hpo_leaderboard1['score_val'][0]) #32.375607#
hpo_model_score2 = abs(hpo_leaderboard2['score_val'][0]) #36.428313#
hpo_model_score3 = abs(hpo_leaderboard3['score_val'][0]) #35.774051#
# Taking the top model score from each training run and creating a line plot to show improvement
# You can create these in the notebook and save them to PNG or use some other tool (e.g. google sheets, excel)
fig = pd.DataFrame(
{
"model": ["initial", "add_features", "hpo1","hpo2","hpo3"],
"score": [baseline_model_score,newfeatures_model_score,hpo_model_score1,hpo_model_score2,hpo_model_score3]
}
).plot(x="model", y="score", ylabel= 'RMSE', style='-*', grid=True, figsize=(8, 6)).get_figure()
fig.savefig('img/model_train_score.png')
# Take the 3 kaggle scores and creating a line plot to show improvement
fig = pd.DataFrame(
{
"test_eval": ["initial", "add_features", "hpo1","hpo2","hpo3"],
"score": [1.80425, 0.70588, 0.49844, 0.46803, 0.46765]
}
).plot(x="test_eval", y="score", ylabel= 'RMSLE',style='-*',grid=True,figsize=(8, 6)).get_figure()
fig.savefig('img/model_test_score.png')
# The 3 hyperparameters we tuned with the kaggle score as the result
pd.DataFrame({
"model": ["initial", "add_features", "hpo","hpo1","hpo2"],
"hpo1": ['default', 'default', 'auto',
"GBM: num_boost_round=80, num_leaves(lower=10, upper=50, default=20)",
"GBM: num_boost_round=80, num_leaves(lower=10, upper=50, default=20)"],
"hpo2": ['default', 'default', 'auto',
"NN_TORCH: num_epochs=10, learning_rate(1e-3, 1e-2, default=5e-3, log=True),activation('relu', 'softrelu', 'tanh'),layers([100],[200, 100])",
"NN_TORCH: num_epochs(lower=8, upper=20, default=10), learning_rate(1e-3, 1e-2, default=5e-3, log=True),activation('relu', 'softrelu', 'tanh'),layers([100],[1000],[200, 100])"],
"scheduler": ['default', 'default', 'ASHA','local','local'],
"trial_time(seconds)": [600, 600,600,600,600],
"score": [1.80425, 0.70588, 0.49844, 0.46803, 0.46765]
})
| model | hpo1 | hpo2 | scheduler | trial_time(seconds) | score | |
|---|---|---|---|---|---|---|
| 0 | initial | default | default | default | 600 | 1.80425 |
| 1 | add_features | default | default | default | 600 | 0.70588 |
| 2 | hpo | auto | auto | ASHA | 600 | 0.49844 |
| 3 | hpo1 | GBM: num_boost_round=80, num_leaves(lower=10, ... | NN_TORCH: num_epochs=10, learning_rate(1e-3, 1... | local | 600 | 0.46803 |
| 4 | hpo2 | GBM: num_boost_round=80, num_leaves(lower=10, ... | NN_TORCH: num_epochs(lower=8, upper=20, defaul... | local | 600 | 0.46765 |
The output column (count - number of total rentals) should not be less than 0 as count can not be negative. So before submiting predictions we need to make sure that our predictions does not contain any negative value in predicted count.
WeightedEnsemble_L2 with feature engineering and Hyper parameter optimization.
I checked correlation matrix of train df and found that initialy most correlated column was temp and realized that temp is not constant for every hour in a day and also for every week might have different avg temp. So I created some additional columns based on datetime provided to fetch hour of day, week of year, weekday etc. and it really helped in improving model performance.
Model improved significantly after adding new features. I think it is due to the quality of feature. By adding creating highly correlated feature it helps model to learn better and find better decision boundary.
Model performance improved with hyper parameter optimization but not that much as it improved by adding new features. From score table you can see by adding new features it improved significantly from 1.8 RMSLE to 0.7, and after HPO it improved by 0.2 RMSLE further.
I would like to spend more time on feature engineering to create new useful features that help model to learn decision boundary for better accuracy. With this I would also like to play with hyperparameters.
| model | hpo1 | hpo2 | scheduler | trial_time (seconds) | score |
|---|---|---|---|---|---|
| initial | default | default | default | 600 | 1.80425 |
| add_features | default | default | default | 600 | 0.70588 |
| hpo | auto | auto | ASHA | 600 | 0.49844 |
| hpo1 | GBM: num_boost_round=80, num_leaves(lower=10, upper=50, default=20) | NN_TORCH: num_epochs=10, learning_rate(1e-3, 1e-2, default=5e-3, log=True),activation('relu', 'softrelu', 'tanh'),layers([100],[200, 100]) | local | 600 | 0.46803 |
| hpo2 | GBM: num_boost_round=80, num_leaves(lower=10, upper=50, default=20) | NN_TORCH: num_epochs(lower=8, upper=20, default=10), learning_rate(1e-3, 1e-2, default=5e-3, log=True),activation('relu', 'softrelu', 'tanh'),layers([100],[1000],[200, 100]) | local | 600 | 0.46803 |


We can improve model performance by spending time on EDA and feature engineering. With better features we can train better models. After training model on data we can then fine tune model by playing with its hyper parameters.